extfieldsofstudy:
Biology
Computer Science
Medicine

text:
Mouse Tumor Biology (MTB): a database of mouse models for human cancer

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

INTRODUCTION
The laboratory mouse has long served as a model system for investigations into human biology and disease because its physiology is similar to that of humans, because there is a high degree of conservation in genes and genome organization, and because the mouse genome is amenable to precise manipulation (e.g. transgenesis, targeted mutation, recombineering, CRISPR/Cas9, etc.) (1)(2)(3)(4). The genetic uniformity of inbred strains contributes to the experimental power of mice to drive discovery of the contributions of specific genes and modifiers for cancer susceptibility and resistance (5). Inbred lines of mice are ideal for identifying and study-ing low penetrance cancer genes (5,6) which are particularly important for uncovering the genetic basis of susceptibility to cancers in human populations when there is no evidence of familial segregation (7). Studies of mice with induced and engineered changes in specific genes or transgenes have provided fundamental insights into the underlying molecular genetics of cancer initiation and progression (8). Although the limits of model systems for the faithful recapitulation of all aspects of human disease must be appreciated (9,10), the laboratory mouse is widely recognized as the premier animal model for investigating clinically relevant aspects of tumor biology (11)(12)(13).
Understanding the influence of genetic background on phenotype variation is critical for the creation of valid mouse models of human cancer and for the appropriate use of these models in cancer research (14). The failure of researchers to appreciate the impact of genetic background can lead to confounding and misleading interpretation of mouse model data. For example, transgenic mice expressing human HRAS on a mixed genetic background of the C57BL/6 and SJL strains were reported to have a mammary gland carcinoma frequency of 45-50% (15). The same transgene allele on an FVB/N strain congenic background resulted in a mammary gland carcinoma frequency of 100% (16). Along similar lines, a recent study reported that both incidence and latency of thymic lymphoma in mouse models varied depending on which genetic background the Atm tm1Awb allele was introgressed into (17). The impact of drug treatments for specific tumor types also needs to be evaluated in the context of what is 'normal' for the genetic background of the mice in the experiment. For example, a study by Roberts et al. (18) demonstrated the importance of genetic background in using animal models to investigate responses to pharmacological intervention.
One of the major trends in translational cancer research is the renewed interest in xenograft models using immunodeficient mice. Although xenografts have been used in cancer research for decades, progressive improvements in the transplant compliant host animal have greatly enhanced the utility of these models in basic and translational cancer research (19). 'Human-in-mouse' xenografts (i.e. Patient- Table 1. Annotation trends in MTB since the project's inception   January 1999  October 2004  October 2009  August 2014   Annotated references  142  1164  2594  3730  Tumor frequency records  3699  23 778  35 832  61 655  Genetically defined strains  623 a  3559 a  3764  6057  Tumors associated with  specific genes   105  800  1330  2288   Images (histopathology,  SKY (20,21). These models have an advantage over cell lines and cell line xenografts because the tumors retain a more natural architecture and are more reflective of the heterogeneity and histology seen in primary tumors (10). They have an advantage over tumors that arise in genetically engineered mice in that the xenografts retain (at least for a period of time following the initial implantation) a human-specific microenvironment. Several murine hosts are used routinely in xenograft studies, including nude (Foxn1 nu ) and SCID (Prkdc scid ). However, much of the current state-of-the-art xenograft research in cancer relies on NOD.Cg-Prkdc scid Il2rg tm1Wjl /SzJ (NOD scid gamma or NSG) mice (20,22). NSG mice lack mature T cells, B cells and functional Natural killer (NK) cells; they are deficient in cytokine signaling. As with genetic models of human cancer, the genetics of the host strain in xenograft models is important for selecting an appropriate model system. Engraftment success of different host strains is variable and an important factor in experimental design of PDX studies (23). Curating this information in the published cancer xenograft literature is one of the future content enhancements planned for Mouse Tumor Biology (MTB) (see Future Directions, below). The MTB (http://tumor.informatics.jax.org) database is a freely available community informatics resource designed to support the effective use of the laboratory mouse as a model system for investigating the genetic and genomic basis of human cancer. MTB provides expertly curated, semantically consistent data to guide researchers in the selection of appropriate strains and mutant mice for experimentation. A fundamental biological principle at the heart of MTB's design and functionality is that the genetic background of mice greatly influences the choice, use, interpretation and translational utility of these models in cancer research. MTB is extending curated content and search functionality beyond the past focus solely on genetic models of human cancer to include mouse models and related data that are important for translational and pre-clinical cancer research. In this paper we describe recent changes to user interface and data acquisition strategies for MTB.

MTB CONTENT
The MTB database was first released on the World Wide Web in 1998 (24) with a primary focus on genetically engineered mouse models of human cancer. Data in MTB related to these models includes frequency of mouse tumors in the context of specific genetic backgrounds, histopathology images of mouse tumors and information about specific mutations/allelic variants in mouse tumors. As genome sequencing became a common approach for characterizing human tumors (25), we developed new interfaces for MTB to connect lists of human genes commonly mutated in various cancers to mouse models carrying mutations in those genes. As PDX have become increasingly important as a platform for basic cancer research and translational cancer research (26), we have initiated curation efforts to represent this literature and these models in MTB. A summary of the trends in MTB's data content since the inception of the resource is provided in Table 1.
Nomenclature and annotation standards are essential for scientific communication and for accurate and complete data retrieval and aggregation (27). The nomenclature and semantic standards implemented in MTB benefit researchers by greatly simplifying what can be a frustrating and time consuming task of finding and comparing data about mouse models. For all of the gene, allele, mutation and strain data in MTB, curators enforce the nomenclature standards set by the International Committee on Standardized Nomenclature for Mice (http://www.informatics.jax. org/nomen/) and the Human Gene Nomenclature Committee (28). An example of the importance of standardized genetic nomenclature is illustrated by alleles of the Fgfr3 gene (fibroblast growth factor receptor 3, MGI:95524). Mouse models carrying mutations in Fgfr3 have been observed to promote skin and lung cancer (29), but finding the relevant model using non-standard nomenclature (e.g. Fgfr3 −/− or Fgfr3 +/− ) is complicated by the fact that there are over 25 targeted alleles of Fgfr3 published, with 11 of the alleles originating from the same laboratory that published the observation of increased tumorigenesis in lung and skin. Only one of the 11 alleles, Fgfr3 tm4Cxd (MGI:2135675), in combination with a targeted mutation in Ctnnb1 (Ctnnb1 tm1Mmt ) and on a mixed genetic background, is associated with increased lung tumorigenesis.
For terminologies associated with tumor classification annotations, MTB adopts standards set by pathologist working groups that are convened on a regular basis to evaluate neoplasias of specific anatomical systems and to de- velop standard diagnoses and terminologies. Examples of standard classifications for mouse tumors to emerge from these workshops include the lymphohematopoietic system (30), lung (31), mammary gland (32), gastrointestinal system (33), nervous system (34), pancreas (35) and prostate (36). We also work with related bioinformatics resources such as PathBase (37) on mapping terms to ensure broad dissemination of standard tumor classification and diagnosis terminologies for mouse models.

DATABASE ENHANCEMENTS
MTB can be searched using web-based search forms and interactive graphical summaries of mouse strain characteristics related to cancer phenotypes (24,38). Four recent enhancements to data content and user interfaces have been implemented in MTB to further advance the mouse for understanding the genetic and genomic basis of cancer. These enhancements include (i) customizable genome maps of Quantitative Trait Loci (QTL) associated with cancer phenotypes mapped in mice, (ii) search tools for finding data sets associated with cancer genomics studies in laboratory mice in public archives, (iii) search tools for finding mouse models using human gene symbols and (iv) access to infor-mation and data associated with PDX models of human cancer.

Cancer QTL in mouse
The laboratory mouse is a powerful genetic model for identifying genes associated with cancer susceptibility and resistance (39). MTB's Cancer QTL Viewer (Figure 1) provides a graphical summary of published cancer-related QTL studies in the mouse that are integrated with the rich biological annotations of mouse genes available from the Mouse Genome Informatics (MGI) database (40). One common starting point for the interface is to display all mapped cancer-related QTL on a genome-wide map. The QTL regions are color coded according to the organ system/cell type associated with the mapping study. In cases where authors provide the symbol of the genetic marker associated with the peak marker-phenotype association score in a mapping study, the QTL is represented by the location of that marker. When authors provide information on the genetic markers that define the boundaries of the QTL region, the entire range of the QTL region is displayed on the map. The cancer QTL graphic in MTB includes a  annotations in a simple GFF-formatted file (General Feature Format; http://www.sanger.ac.uk/resources/software/ gff/) so they can be displayed in relationship to published QTL regions. By highlighting a sub region of a chromosome of interest, users are directed to another workspace where they can filter the genes in a region according to the biological (phenotype and function) annotations of those genes. For example, a researcher could identify a region of the genome where multiple lung cancer QTL have been mapped and then filter the genes in that region to find those previously associated with lung phenotypes, biological processes or molecular functions.

Cancer genomics
Genomics technologies are now used routinely to characterize the genomes of primary human tumors and of tumors in genetically defined mouse models. Although many of the genome-scale data from these studies are available from public data archives it can be difficult for researchers to find data sets that include specific mouse strain information or are associated with specific tumor types due to the lack of adherence to standardized gene and strain nomenclature. To support rapid access to mouse cancer genomics data, MTB indexes public genomic data archives such as Gene Expression Omnibus (GEO) (41) and ArrayExpress (42) with standardized nomenclature. The Gene Expression Data Set Search Form in MTB allows users to search for data sets in these archives by organ, tumor classification, strain name and assay platform. The search results allow users to rapidly access study information and the data for each sample in the relevant public archive and are also linked to relevant mouse model data in MTB.
To link the results of large-scale human cancer genomics studies to mouse models, MTB curators have compiled lists of human cancer genes identified from published cancer genome surveys. These lists are provided on the Human Gene Search form in MTB and can be downloaded or used as input to search the MTB for mouse models associated with each of the genes in the list. The search form also allows ad hoc searches of mouse models in MTB using human gene symbols. An example of a search of MTB using the precompiled list of 26 human genes identified as being frequently mutated in a survey of 188 human lung adenocarcinomas (43) is shown in Figure 2.

PDX
MTB supports online access to all of the available PDX models from The Jackson Laboratory's PDX Resource. The models in this resource have been comprehensively annotated for clinical information (de-identified) about the patients from whom the primary tumor material was obtained, histopathology and diagnostic marker labeling, whole genome copy number variation (CNV), genome-wide transcriptional profiling and targeted exome sequencing. Subsets of the models have tumor growth data and standard of care drug response data from dosing studies in tumorbearing mice. When histopathology images of both the primary patient tumor and the engrafted tumors are available, a board certified pathologist reviews the images and provides a summary of the degree to which there is concordance of the morphological features between the primary and engrafted tumors. The user interfaces to the PDX associated data support searches for models by cancer type, patient diagnosis and genomic properties of the engrafted tumor (expression, CNV and/or mutation) (Figure 3). Tabular summaries of variants identified in the engrafted tumors and graphical summaries of gene expression, amplifications and deletions are also provided for each model. Users can request information on the availability of tumor fragments and tumor-bearing mice using a web-based form that is forwarded to the customer services group at The Jackson Laboratory.

FUTURE DIRECTIONS
MTB initially was designed to serve as a centralized resource of information regarding mouse models of human cancer, with a focus on emphasizing how the genetic background of different strains of laboratory mice can influence cancer phenotypes in genetically engineered mice. Mice as genetic models of human cancer will continue to be a focus for the resource and we will integrate two emerging sources of data in this area in the near future. First we will curate cancer-related phenotype information in data emerging data from large-scale phenotyping initiatives associated with the international Knockout Mouse Project (44,45). A second source of new data in MTB will come from complex trait mapping studies using new high-precision mapping populations such as Diversity Outbred mice (46). While mouse model representation will continue to be a primary and unique focus for the MTB database project, many of the planned enhancements to the resource will em-Nucleic Acids Research, 2015, Vol. 43, Database issue D823 phasize promoting the use of mouse models in translational and pre-clinical cancer research. We will accomplish this goal by highlighting data used to validate the human cancer relevance of mouse models (i.e. data associated with model 'credentialing') and by adding PDX models and data. Specific planned enhancements to MTB include interfaces that highlight the genomic and histopathology concordance of mouse models and human cancers. We also are working with other informatics groups to develop improved interfaces for navigating conserved syntenic regions of the mouse and human genome, enabling better integration of complex trait mapping studies in the mouse with genome-wide association study data in humans. The representation of PDX models in MTB is currently centered on The Jackson Laboratory's PDX Resource, however we will incorporate information and data from other PDX resources and repositories when possible.

INFRASTRUCTURE
The MTB database system has three major software components: 1) The public web interface runs from version 5.x of the Apache Jakarta Tomcat Web Server and utilizes the Apache Struts Framework. The web interface utilizes Java technology conforming to the 2.4/2.0 Java Servlet/Java Server Pages specifications. We leverage Ex-tJS and Google Visualizations Javascript libraries for data visualization on the public web interface to MTB. 2) The curatorial interface for MTB is a Java-based desktop application used by the Scientific Curators to enter and curate data. Curator interfaces are built using a rich set of custom Java libraries designed around the Core J2EE Design Patterns.
3) The database itself is a highly normalized relational database currently housed in MySQL. JDBC is used to access the data repository The MTB Application Programming Interface (API) facilitates the exchange of data with users and with other database systems. In keeping with our software design goals of 'write once, run anywhere', Web Services via SOAP is the primary access to the MTB API suite. This design supports access to MTB data in a platform and language independent manner that suits the differing needs, standards and tools of the community.

USER SUPPORT
The

DATA USE AND RESOURCE CITATION
MTB data and software are provided freely to the scientific community for promoting education and research activities. Any reproduction or use for commercial purpose is prohibited without the prior express written permission of The Jackson Laboratory. Users of MTB are encouraged to cite this paper when referring to MTB in a publication. The following format is suggested when referring to specific data obtained from


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Chemistry

text:
Interaction of Daptomycin with Lipid Bilayers: A Lipid Extracting Effect

Daptomycin is the first approved member of a new structural class of antibiotics, the cyclic lipopeptides. The peptide interacts with the lipid matrix of cell membranes, inducing permeability of the membrane to ions, but its molecular mechanism has been a puzzle. Unlike the ubiquitous membrane-acting host-defense antimicrobial peptides, daptomycin does not induce pores in the cell membranes. Thus, how it affects the permeability of a membrane to ions is not clear. We studied its interaction with giant unilamellar vesicles (GUVs) and discovered a lipid-extracting phenomenon that correlates with the direct action of daptomycin on bacterial membranes observed in a recent fluorescence microscopy study. Lipid extraction occurred only when the GUV lipid composition included phosphatidylglycerol and in the presence of Ca2+ ions, the same condition found to be necessary for daptomycin to be effective against bacteria. Furthermore, it occurred only when the peptide/lipid ratio exceeded a threshold value, which could be the basis of the minimal inhibitory concentration of daptomycin. In this first publication on the lipid extracting effect, we characterize its dependence on ions and lipid compositions. We also discuss possibilities for connecting the lipid extracting effect to the antibacterial activity of daptomycin.

D aptomycin (Figure 1) is an FDA (the U.S. Food and Drug Administration)-approved lipopeptide antibiotic, notably active against multidrug-resistant, Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). In recent reviews, 1−3 it was concluded that despite nearly 25 years of study and 9 years of clinical use, aspects of the mechanism of action of daptomycin remained poorly understood. The evidence so far suggests that its main target is the cytoplasmic membrane, where daptomycin causes leakage of cations leading to loss of membrane potential and cell death. 1,2,4−6 The membrane function is compromised in the absence of cell lysis or leakage of molecules other than atomic cations. 6,7 What is not yet clear is what molecular process underlies the action of daptomycin. One major concern about the clinical use of daptomycin is the development of resistance during therapy. Many of the mutations that alter susceptibility to daptomycin have been shown to directly affect the membrane lipid composition. 8−12 This further supports the role of the membrane being the central target for the action of daptomycin. Thus, the interaction of daptomycin with lipid bilayers is fundamental to its antibiotic activity.
The antibacterial activity of daptomycin is calcium-dependent 13 and correlates with the target membrane's content of phosphatidylglycerol (PG). 10 In the presence of Ca 2+ ions, daptomycin spontaneously binds to artificial membranes, 14 but only minor effects of daptomycin on lipid vesicles have been detected in past experiments. 14−16 Fluorescence resonance energy transfer (FRET) experiments have shown daptomycin aggregation in membranes, 17−19 but no leakage of calcein from lipid vesicles was detected. 15 The only noticeable effect of daptomycin on the membrane properties was causing leakage of cations from lipid vesicles, but the effect was weak compared with that of an ionophore valinomycin at comparable concentrations. 20 These earlier studies used artificial membranes in the form of small liposomes (≲100 nm). [14][15][16]18,21 Here we used giant unilamellar vesicles (GUVs) (>10 μm) that allowed us to observe the action of daptomycin under a microscope. Our results are consistent with all previous measurements, but in addition, we discovered an effect of lipid extraction that appears to be similar to the direct action of daptomycin on bacterial membranes. 1 Lipid extraction occurred only when the GUV lipid composition included phosphatidylglycerol (PG) and in the presence of Ca 2+ ions, exactly the same condition found to be necessary for daptomycin to be effective against bacteria. The greatest advantage of the GUV method over the methods of smaller liposomes is that we can measure the membrane area change as daptomycin binds to it. Thus, we obtained a quantitative measure of the lipid extracting effect and showed that it has a daptomycin concentration dependence. We found that lipid extraction by daptomycin is a cooperative phenomenon with a threshold concentration. The effect was detected only for daptomycin concentrations above this threshold.
Because daptomycin targets bacterial cell membranes, the resistance mechanisms have been suspected to involve changes in the lipid composition of the bacterial cell membrane. 9 One possibility is decreasing the extent of the synthesis of PG or increasing the extent of the conversion of PG to lysylphosphatidylglycerol (LPG). 9 These were found in the resistant mutants of Staphylococcus aureus 22 and Bacillus subtilis. 23 Our experiment shows that replacing PG with LPG has the effect of diminishing the lipid extracting effect. Another possibility involves cardiolipin. 12 Recent reports suggested interference of cardiolipin with the action of daptomycin on membranes. 24 Aspirated GUV Experiment. This experiment was a modification of the original method of Kwok and Evans 26 and of Longo et al. 27 as described by Sun et al. 28 Giant unilamellar vesicles (GUVs) of a chosen lipid composition were produced by the electroformation method 29 in a solution containing 199 mM sucrose for the purpose of controlling the osmolality and 1 mM Tris (pH 7). Ten microliters of a GUV suspension was injected into a control chamber that contained 190 mM glucose and 10 mM Tris (pH 7). A selected GUV (diameter of ∼20−30 μm) was aspirated at a low constant negative pressure (∼100 Pa producing a membrane tension of ∼0.4 mN/m) with a micropipette, which was connected to an oil-filled U tube in which a negative pressure was produced and controlled by reference to the atmospheric pressure. 28 A separate observation chamber contained ∼190 mM glucose, 10 mM Tris (pH 7), Ca 2+ ions, and daptomycin. The aspirated GUV was then transferred through a movable pipe 28 from the control chamber to the observation chamber. We used sucrose inside and glucose outside of GUV, so that GUVs sank to the bottom for ease of aspiration manipulation. Also the phase contrast between sucrose and glucose could be monitored by a phase condenser to detect possible leakage of sugar through the membrane. We included a small amount of dye lipid (1 mol % Rh-PE) to make the boundary of the GUV clear, except in the case of the dye leakage experiment in which 10 μM Texas Red sulfonyl chloride was encapsulated in the GUV. The osmolality of each solution used in the experiment was measured by the Wescor (Logan, UT) model 5520 vapor pressure osmometer. Equi-osmolality between the inside and outside of the GUV was the initial condition in all GUV experiments. The response of the GUV to daptomycin binding was observed and recorded by a camera-equipped microscope. All GUV experiments were performed at room temperature (∼25°C).
The binding of daptomycin to a GUV can be monitored by the expansion of the membrane area. This expansion is too small to be measured accurately by the change in the diameter of GUV. We used a technique developed by Kwok and Evan,26 in which a spherical GUV was aspirated partially into a micropipette by a constant, low membrane tension (∼0.4 mN/ m) (Figure 2A). The shape of the GUV then consisted of two parts, a spherical part and a cylindrical part, with different surface area/volume (A/V) ratios. An overall A/V change due to peptide binding would cause a repartition between these two parts, from which the change in area could be accurately measured. From the microscopic images, the length of the protrusion into the micropipette (L p ), the radius of the micropipette (R p ), and the radius of the GUV (R v ) were carefully measured. Then it is straightforward to show ΔA = 2πR p ΔL p + 8πR v ΔR v and ΔV = πR p 2 ΔL p + 4πR v 2 ΔR v . 26 If there were no molecular leakage (see below) and as long as the inside and outside of the GUV had the same osmolality, there would be no change in volume. When ΔV = 0, ΔA is directly proportional to ΔL p : ΔA = 2πR p (1 − R p /R v )ΔL p . The fractional area change (ΔA/A) is calculated from the change in protrusion length ΔL p . When the protrusion length decreased, the data analysis was limited to the point at which the protrusion length vanished. The maximal negative ΔA/A value depended on the initial protrusion length (before the exposure to daptomycin) that varied from vesicle to vesicle. All experiments reported here were highly reproducible, although small quantitative variations were expected of GUV experiments. For the sake of clarity, only two or three representative results are presented in the figures for each type of experiment, approximately 20−30% of actual measurements. No error bars are shown because the results are interpreted qualitatively, except for the measurement of the threshold concentration.

■ RESULTS
Lipid Extracting Effect. The antibacterial activity of daptomycin requires Ca 2+ ions. 13 The standard media for in vitro susceptibility testing of daptomycin in clinical laboratories are set to a calcium content of 50 mg/L (∼1.25 mM), 30 which is close to the calcium concentration in human serum. 2 The reported minimal inhibitory concentration (MIC) values of daptomycin are in the micromolar range. 2,13 Thus, we chose 1 μM daptomycin and 1 mM Ca 2+ as our reference condition (we found our results to be insensitive to Ca 2+ concentration from ∼0.5 to ∼2 mM). Also, the susceptibility of bacteria to daptomycin is correlated to the target membrane's content of phosphatidylglycerol (PG). 9,23 To simulate the PG content of bacterial membranes, we used lipid bilayers of a DOPC/DOPG mixture at a 7/3 ratio. PC/PG mixtures were commonly used in previous model membrane experiments 15,17,18 for daptomycin.
First, for the control experiments, in the absence of daptomycin, we detected no membrane expansion of a 7/3 DOPC/DOPG GUV with Ca 2+ ( Figure S1 of the Supporting Information). Figure 2A shows the responses of the GUVs to the binding of daptomycin. The GUVs were made visible by including a small fraction (1 mol %) of 18:1 Liss Rhod PE (Rh-PE). They were transferred at time zero into an observation chamber containing a solution of daptomycin and Ca 2+ ions (see Materials and Methods for the exact solution compositions). In a solution of 0.3 μM daptomycin, the peptide binding caused the GUV surface area to expand initially (see Figure 2B for ΔA/A values calculated from the increasing protrusion length ΔL p ). Gradually, the area expansion saturated at a maximum ΔA/A value of ∼3% and showed no further changes. In contrast, when the daptomycin concentration was higher than 0.3 μM, the membrane area expansion first reached a maximum and then the area decreased ( Figure 2). During the membrane area decrease, we detected lipid−peptide aggregates (see below) exuded from the outer surface of the GUV and most of the aggregates appeared to stay on the outer surface (Figure 2A,C). The phenomena are most clearly seen in the movies of the GUV images that show the movement of exuded substances (see Movies S1 and S2 of the Supporting Information). The aggregates were the end products of the reaction. The net effect on the GUV was that lipid molecules were removed or extracted from the lipid bilayer during the reaction and the amount of lipid in the GUV bilayer was reduced. Because the most important consequence of the interaction is the effect on the membrane, we will call this phenomenon the lipid extracting effect (see Note S1 of the Supporting Information).
To show that the aggregates are those of both lipid and daptomycin, we performed the same experiment with daptomycin, including 30% fluorescence-labeled BODIPYdaptomycin. (Note that BODIPY-daptomycin is 2−4 times less active than the unmodified molecule. 1 ) Figure 3 shows that the exuded aggregates contained both Rh-PE and BODIPYdaptomycin.
To show that the protrusion length decrease was indeed due to lipid extraction, not caused, for example, by a GUV volume increase due to any exchange of solute between the inside and outside of the GUV, 28 Figure  2 (compare with the data for 1 μM daptomycin); in both cases, ΔA/A decreased from a maximum by more than 4% that could be due to only lipid extraction.  Figure 2 with fluorescent daptomycin shown as confocal images. A 7/3 DOPC/DOPG GUV (including 1% Rh-PE) was introduced at time zero into a solution containing 1 μM daptomycin with 0.5 μM BODIPY-daptomycin and 1 mM Ca 2+ . To show colocalization of lipid and peptide in aggregates, confocal images were taken: red for RH-PE and green for BODIPY-daptomycin. The GUV protrusion length increased and then decreased as in Figure 2. As the protrusion length decreased, aggregates appeared on the GUV surface containing both lipid and peptide. The scale bar is 10 μm.

Molecular Leakage Experiment.
To test if the lipid extracting effect causes any molecular leakage, we included 10 μM Texas Red sulfonyl chloride (TRsc, MW 625) in the GUV. In this case, no dye lipid (Rh-PE) was used, because Lissamine Rhodamine B (Rh) and Texas Red are of the same color; consequently, the aggregates were not directly visible ( Figure  5). The change in the slope of ΔA/A signaled the occurrence of lipid extraction, but there was no leakage of the content dye. The fluorescence intensity of the encapsulated TRsc decreased slightly over time at a rate comparable to that of photobleaching of the background fluorescence. For comparison, the TRsc leakage caused by melittin pores is shown in Figure S2 of the Supporting Information.
Ion Dependence. What is the role of Ca 2+ ions in the action of daptomycin on membranes? In the absence of Ca 2+ ions, no binding of daptomycin was detected at a concentration of 1 μM (not shown). Even when Ca 2+ was replaced with Mg 2+ (at 1 mM), we saw little binding of daptomycin to the PGcontaining membranes ( Figure 6). This is consistent with the reports that the antibacterial activity of daptomycin requires Ca 2+ ions 13 and that the MICs increased at least 32-fold if Ca 2+ ions were replaced with Mg 2+ ions. 21 However, if the daptomycin concentration was increased to 5 μM, there was a membrane area expansion indicating daptomycin binding at higher concentrations in the absence of Ca 2+ ions. At 50 μM daptomycin without Ca 2+ ions, the response of the GUV was similar to that with 1 μM daptomycin with Ca 2+ (Figure 2). This result indicates that the role of Ca 2+ is to facilitate the binding of daptomycin to lipid bilayers, as suggested in an earlier study, 15 but the binding affinity of daptomycin for membranes is not zero in the absence of Ca 2+ . As long as there is a sufficient amount of daptomycin bound to a PG-containing membrane, there is a lipid extracting effect. Again, this is consistent with the MICs increasing 32-fold in the absence of Ca 2+ ions. 21 Lipid Dependence. The susceptibility of bacteria to daptomycin is correlated with the membrane content of PG. 9 Indeed, we found that the lipid extracting effect occurred only when GUVs contained PG. Interestingly, daptomycin binds to the electrically neutral pure DOPC membrane in the presence of Ca 2+ but exhibited no lipid extraction (Figure 7). The peptide moiety of daptomycin is anionic [net charge of approximately −3 at pH 7 ( Figure 1)], yet it still has a low binding affinity for the DOPC/PG anionic lipid bilayer in the absence of Ca 2+ (Figure 6). With Ca 2+ , daptomycin has a stronger affinity for DOPC than for DOPC mixed with anionic CL or LPG (Figure 7). Thus, the binding affinities of daptomycin for lipid bilayers do not follow a naive electrostatic argument based on the net charge.
Some studies of the resistance to daptomycin implied the possibilities of shifting the phospholipid composition from PG to LPG in one case 9,22,23 and to CL in another. 11,12 We found that replacing PG with either LPG or CL eliminated the lipid extracting effect at a daptomycin concentration of 1 μM ( Figure  7). However, the most recent studies implied possible interference of CL with the action of daptomycin. 24,25 This clearly calls for much more extensive studies of daptomycin with the lipid mixtures of CL and PG.
Existence of a Threshold Concentration. The lipid extracting effect has a strong daptomycin concentration dependence. Below a threshold concentration, the effect was absent. Above the threshold, the effect occurred in a shorter and shorter time as the daptomycin concentration increased. To quantify this dependence, we plotted the time τ for reaching the maximum of ΔA/A [(ΔA/A) max ] as a function of daptomycin concentration ( Figure 8). As the daptomycin concentration decreased from 4 to 0.4 μM, the time to reach (ΔA/A) max exponentially increased. When the concentration decreased to 0.3 μM, the membrane area increased to ∼3% and  We examined all GUV experiments with a 7/3 DOPC/ DOPG GUV and Ca 2+ ions and concluded that there is a critical value of (ΔA/A) max of ∼3%. [This is the average of (ΔA/A) max values for daptomycin at 0.4−0.5 μM.] At <0.4 μM daptomycin, ΔA/A reaches a constant value of ≲3% without a lipid extracting effect. At >0.4 μM daptomycin, ΔA/A increases to a (ΔA/A) max of >3% and then decreases, concurrent with a lipid extracting effect. The lipid extracting effect started when the membrane expansion ΔA/A reached the critical value of ∼3%. However, at high daptomycin concentrations, the membrane area continued to increase even after the lipid extracting effect had started, because daptomycin continued to bind to the lipid bilayer from the solution. Consequently, the (ΔA/A) max value could reach ∼6% before the membrane area started to decrease showing the effect of lipid extraction ( Figure  2B).

■ DISCUSSION
Daptomycin is the first approved member of a new structural class of antibiotics, the cyclic lipopeptides (Figure 1). It is a water-soluble membrane-acting antibiotic. 1,2,4−6 The apparent membrane-acting feature of the molecule is its hydrophobic N-terminal decanoyl chain. It is worth noting that the decanoyl chain can be replaced by varieties of hydrophobic substitutes without losing antibacterial activity. 18,32 The hydrophobic moiety is expected to insert into the hydrocarbon region of a membrane. However, the research so far has not yielded the molecular configuration of membrane-bound daptomycin. There is a change in the circular dichroism (CD) 15 of daptomycin when it binds to PG-containing lipid bilayers in the presence of Ca 2+ , indicating a possible structural change upon membrane binding, but there has been no theoretical interpretation of the CD spectra of daptomycin. Nuclear magnetic resonance studies were limited to daptomycin in solutions without lipid bilayers. 15,33,34 The lack of structural information about membrane-bound daptomycin is the main obstacle for understanding its molecular mechanism.
Over the past two and a half decades, the field of membraneactive peptides has grown from the studies of host-defense antimicrobial peptides (AMPs). 35−41 AMPs are characterized by their abilities to form pores in membranes and cause leakage of dye molecules from lipid vesicles. 35−41 Thus, very active research of AMPs has focused on the molecular mechanisms of AMPs that cause molecular leakage. 35−41 In contrast, daptomycin does not induce molecular leakage across a membrane; 6,7 the proof is shown here in Figure 5 (and Figure  S2 of the Supporting Information; see Note S2 of the Supporting Information). Compared with the research publications for AMPs, there are fewer studies of daptomycin−membrane interactions because of the lack of obvious effects on the membrane. Until now, the only detected reaction  in membranes is oligomerization or aggregation of daptomycin by the FRET technique. 17,18 A hypothesis for transmembrane octameric daptomycin pores has been postulated from the FRET and ion leakage experiments. 19,20,25 This hypothesis required many assumptions 19,20,25 that have not been critically evaluated. There is no structural evidence of such pores, and there are no free energy arguments about why daptomycin, given its molecular structure, would form such an octameric complex and why such a complex would be stable inside a lipid bilayer. In contrast, pores induced by AMPs have been detected by X-ray diffraction in lipid crystallines 41,42 and by neutron scattering in fluid membranes, 43,44 and they are free energy minimum states (see references in refs 40 and 41).
Here we discovered an effect of daptomycin on lipid bilayers that appeared to be similar to the direct action of daptomycin on bacterial membranes observed in a recent fluorescence microscopy study, 1 i.e., inducing lipid aggregates on the surface of a bacterial membrane. We emphasize that the observed aggregates are the end product of the daptomycin interaction. We imagine that the aggregates must have started from a microscopic size and became visible only after they grew or combine to micrometer size. With a microscope, we cannot see the aggregation process. Most of the macroscopic aggregates appeared to stay on the GUV surface. They moved away from the GUV surface if there was a flow in the solution (see Movies S1 and S2 of the Supporting Information). As far as we could detect, the aggregates staying on the surface of GUV had no effect on the property of the GUV bilayer. Therefore, we believe that the main effect of daptomycin on the membrane is removing lipid molecules from the bilayers as a result of the interaction.
The lipid extracting effect is a predictable reaction of daptomycin with lipid bilayers under specific conditions. It occurred only when the GUV lipid composition included PG and in the presence of Ca 2+ ions, the same condition found to be necessary for daptomycin to be effective against bacteria. 10, 13 Most surprisingly, the lipid extracting effect has an all-or-none cooperative concentration dependence that is similarly to a MIC. This suggests that the lipid extracting effect and the antibacterial activity of daptomycin are correlated. The current consensus view of daptomycin's bactericidal activity is that it causes leakage of atomic cations through the cytoplasmic membrane of bacteria, leading to loss of membrane potential and cell death. 1,2,4−6 It compromises the membrane function without cell lysis or molecular leakage. 6,7 Can the lipid extracting effect be the underlying mechanism for the bactericidal activity? We discuss this possibility by offering the following observations.
Lipid Extraction and Ion Leakage. The permeation of a membrane by ions is one of the subjects most studied by molecular simulations. 45−49 The consensus results can be summarized as follows. (i) Ion permeation is an activated process; spontaneous ion permeation is rare. 48,49 (ii) Ions enter the bilayer with a trail of water molecules called water fingers 45 (the potential energy for an isolated ion in membrane is too high). The water finger is lined by lipid headgroups. This results in a major rearrangement of neighboring lipids, commonly called water pore defects. 45−49 (iii) Simulations by displacing a pair of chain-to-chain lipid molecules from a bilayer create a water pore defect, allowing ion permeation. 47 (iv) The transient water pores created by activated ion permeation or by lipid molecule displacement have radii of <1 nm 46 and lifetimes of 10−100 ns. 46,47 Thus, it is reasonable to expect that daptomycin-induced lipid extraction can lead to ion permeation. So far, daptomycininduced K +4 and Mg 2+6 leakage has been observed in bacteria. The negative counterions were most likely blocked from permeation by the presence of anionic lipid PG. The water pore defects induced by the lipid extracting effect would be too transient and too small to allow molecular leakage. It is wellknown that very small amounts of net charge translocation can cause significant changes in membrane potential; e.g., it has been demonstrated that the translocation of only 2.9 × 10 3 H + ions per Escherichia coli cell was sufficient to change the membrane potential by 16 mV. 50,51 Molecular Process That Causes Lipid Extraction. The lipid extraction effect is not unique to daptomycin. Similar effects have been observed previously in the studies of βamyloid peptides human islet amyloid polypeptide (hIAPP) 52 and amyloidogenic peptide penetratin. 53,54 Despite having different molecular structures, daptomycin and β-amyloid peptides exhibit similar behaviors in solution. β-Amyloid peptides are monomeric at low concentrations but aggregate at high concentrations. 55 Similarly, daptomycin exists as soluble monomers at micromolar concentrations 17 but oligmerizes at millimolar concentrations. 15,21,33 In the presence of lipid bilayers, β-amyloid peptides first bind to bilayers as α-helical monomers, but when the bound peptide/lipid ratio exceeds a threshold, peptides transform from α-monomers to β-sheet aggregates (can be detected by CD). 52−54 The latter appeared as peptide−lipid aggregates on the membrane surface, 52,53 exactly like the aggregates induced by daptomycin. Normally, a molecule bound to a lipid bilayer either adsorbs on the interface between the headgroups and the chains or embeds inside the chain region. Both the β-peptide aggregates and the daptomycin aggregates unbind from the lipid bilayer, because, we believe, they are not compatible with being a bound state in lipid bilayers. It is not clear in either case whether lipid is intrinsically part of the molecular aggregation or lipid molecules attach to the peptide aggregates when the latter unbind from the bilayer. In either case, the actions of molecular aggregation and unbinding from the bilayer have an effect of lipid extraction, i.e., removing lipid molecules from the bilayer.
The connection of the lipid extracting effect to daptomycin's bactericidal activity is reinforced by the findings that hIAPP and penetratin are cytotoxic 56,57 and antibacterial, 58,59 respectively. They were also found to induce transmembrane ion conduction without causing molecular leakage. 53

Notes
The authors declare no competing financial interest.


s2fieldsofstudy: Biology Medicine

text:
Zinc–rs13266634 and the Arrival of Diabetes Pharmacogenetics: The “Zinc Mystique”

Advances in our understanding of the genetics of type 2 diabetes have been astronomical over the past decade with more than 100 single nucleotide polymorphisms (SNPs) associated with modest increases in diabetes risk and differences in related traits such as fasting glucose (1,2). However, these SNPs have contributed more to elucidating biologic pathways than predicting diabetes risk (3). Nonetheless, the most promising work suggests that we are on the cusp of being able to use genetic information to individualize treatment for type 2 diabetes prevention and management (4,5). The promise of pharmacogenetics is predicated on the existence of known individual variation in response to interventions and the strong biologic basis for genetic variation as a primary, immutable difference between individuals.

rs13266634 is one of the most consistently replicated diabetes risk variants (odds ratio of 1.14 for the R allele) (6). This SNP encodes a C→T base substitution in the SLC30A8 gene, resulting in a change in the coded protein (Arg325Trp or R325W). The frequency of the diabetes risk R allele is 91.5%, 71.7%, and 56.7% in Africans, Europeans, and Asians, respectively (1,000 Genomes).

The SLC30A8 gene encodes zinc transporter-8 (ZnT8). This transporter is specific to pancreatic islets and is …

Advances in our understanding of the genetics of type 2 diabetes have been astronomical over the past decade with more than 100 single nucleotide polymorphisms (SNPs) associated with modest increases in diabetes risk and differences in related traits such as fasting glucose (1,2). However, these SNPs have contributed more to elucidating biologic pathways than predicting diabetes risk (3). Nonetheless, the most promising work suggests that we are on the cusp of being able to use genetic information to individualize treatment for type 2 diabetes prevention and management (4,5). The promise of pharmacogenetics is predicated on the existence of known individual variation in response to interventions and the strong biologic basis for genetic variation as a primary, immutable difference between individuals.
rs13266634 is one of the most consistently replicated diabetes risk variants (odds ratio of 1.14 for the R allele) (6). This SNP encodes a C→T base substitution in the SLC30A8 gene, resulting in a change in the coded protein (Arg325Trp or R325W). The frequency of the diabetes risk R allele is 91.5%, 71.7%, and 56.7% in Africans, Europeans, and Asians, respectively (1,000 Genomes).
The SLC30A8 gene encodes zinc transporter-8 (ZnT8). This transporter is specific to pancreatic islets and is mainly expressed in b-cells that transport zinc from the cytoplasm into insulin secretory vesicles (7). This zinc stabilizes insulin hexamers, prevents insulin degradation, and is cosecreted with insulin, proinsulin, and C-peptide during the early stage of glucose-stimulated insulin secretion (GSIS) (7,8). Cosecreted zinc also passes through the portal vein with the other contents of the insulin secretory vesicle and appears to inhibit hepatic clearance of insulin (8). Although preclinical studies consistently show structural abnormalities of insulin secretory vesicles in the absence of ZnT8 (9-11), they are not consistent regarding the impact of ZnT8 abnormalities on GSIS (9)(10)(11)(12). The few human studies that evaluated quantitative traits suggest that carriers of the R risk allele have abnormalities in both GSIS and insulin processing (13)(14)(15)(16).
In this issue, Shan et al. (17) suggest that plasma zinc concentrations are associated with diabetes in a genotypespecific fashion, a finding that highlights the potential of rs13266634 as a strong genetic candidate for individualizing diabetes care. The authors first measured zinc levels in 1,776 Chinese people with normal glucose tolerance (n = 793), newly diagnosed impaired glucose tolerance/ impaired fasting glucose (n = 218), and newly diagnosed type 2 diabetes (n = 785) and observed lower plasma zinc concentrations in those with diabetes. The novel finding, however, was a statistical interaction between zinc levels and rs13266634 such that the benefit of higher zinc levels on the odds of diabetes decreased with increasing copies of the R (risk) allele (WW.RW.RR).
Shan et al. provide the first published data on the potential interaction between zinc levels and rs13266634 on diabetes risk. A major strength of the new report is its focus on a biologically plausible interaction: rs13266634 is a coding variant that affects a specific zinc transporter that is directly involved in insulin secretion in the b-cell. One could therefore hypothesize that the impact of available zinc would vary across its genotype. In fact, these findings are consistent with a prior meta-analysis of five cross-sectional studies that showed higher zinc intake seemed to reduce the fasting glucose-raising effect of rs11558471; rs11558471 is an SLC30A8 SNP in tight linkage disequilibrium with rs13266634 (18). Of course, plasma zinc levels are unlikely to reflect zinc exposure at the level of the b-cell, and zinc levels assessed at a single point in time may not reflect the zinc relevant to the biological interaction important for understanding the potential therapeutic application of zinc for diabetes prevention. More important, these findings need to be replicated with larger sample sizes that assess zinc status prior to diabetes diagnosis, an approach that will address reverse causality.
If these associations hold up, the most logical and useful extension of this work would be a clinical trial of zinc supplementation to evaluate its therapeutic potential and safety, ultimately aiming for prevention or treatment of type 2 diabetes based on rs13266634 genotype. At a population level, understanding the implications of dietary patterns (and their zinc content) on this interaction is also extremely important given that other micronutrients, including iron and copper, are also important for the action of zinc in the body (19). Also, zinc is ubiquitous throughout the body. Supplementation could therefore have far-reaching effects because zinc serves as an essential component of numerous proteins, including enzymes, transcription factors, and growth factors, and it acts independently in autocrine, paracrine, and neurotransmitter roles (20).
The early chapters of the zinc-rs13266634 story raise several questions for diabetes genetic epidemiology, especially issues related to nutrigenetics and pharmacogenetics. Historically, zinc has been of interest in diabetes because it has been linked to lower diabetes risk in observational studies (21), and has reduced fasting glucose and hemoglobin A 1c in some randomized trials (22). The interaction under study may explain some of this "zinc mystique." However, while accumulating evidence suggests that rs13266634 impacts diabetes risk and insulin secretion traits in humans and is affected by total zinc intake and circulating zinc levels, the functional implications of the rs13266634 variant remain unclear. Indeed, a recent study found that rare loss-of-function variants in SLC30A8 are actually protective against diabetes (23), and another suggests that rs13266634 may impact regulation of hepatic insulin clearance (8). If the findings of Shan et al. (17) are confirmed and genotypeguided zinc interventions show promise for diabetes, at what point do we consider the evidence to be substantial enough to warrant clinical use? And how will we implement this individualized care? These and other questions are ripe for investigation.


extfieldsofstudy:
Engineering
Computer Science
Medicine

text:
Lab-on-Chip Cytometry Based on Magnetoresistive Sensors for Bacteria Detection in Milk

Flow cytometers have been optimized for use in portable platforms, where cell separation, identification and counting can be achieved in a compact and modular format. This feature can be combined with magnetic detection, where magnetoresistive sensors can be integrated within microfluidic channels to detect magnetically labelled cells. This work describes a platform for in-flow detection of magnetically labelled cells with a magneto-resistive based cell cytometer. In particular, we present an example for the validation of the platform as a magnetic counter that identifies and quantifies Streptococcus agalactiae in milk.

Introduction
Flow cytometry is a technique that enables the measurement of morphological, biochemical and functional characteristics of microscopic particles (cells, viruses, bacteria, yeast, intracellular organelles, aerosol particles or microbeads (from 0.2 μm to 150 μm diameter) suspended in a stream of fluid. It allows the characterization and quantification of a population at high rates and with a high-throughput [1]. The multiparametric analysis of the particles can be performed by light scattering or staining the particles with fluorophores (fluorescent conjugated antibodies or absorption dyes) or quantum dots and presenting the particles, one by one, to a laser beam of a single wavelength or arc lamp [1][2][3].
For the past three decades, advances in sample pre-treatment, flow handling, precision technologies, synthesis of emitting particles, data handling techniques and bioinformatics have allowed the introduction of this sophisticated analytical tool into routine clinical and laboratory use in cell/molecular biology fields [4][5][6], formulation and biotesting of compounds [7,8], disease diagnosis [9][10][11], immunology [12,13], genetics [14], industrial bioprocesses [15], and environmental monitoring [2,16,17]. In addition to detection and enumeration, some flow cytometers have the ability to sort cells at high speeds based on detected signals without loss of viability or particle-specific characteristics [3].
Although conventional state-of-the-art flow cytometry systems provide rapid and reliable analytical results, and despite the considerable recent technological advances, these devices are still bulky, expensive and complex. Over the past years, the drawbacks of conventional flow cytometers have encouraged efforts to take advantage of microfabrication technologies and advanced microfluidics to achieve smaller, simpler, more innovative and low-cost instrumentation with enhanced portability for on-site measurements. This miniaturization approach has in general made use of inexpensive polymers such as polydimethylsiloxane (PDMS) [18] and detection techniques easily integrated with electronics [19], such as optical fibers [20], CCD cameras [21], diode lasers [22,23], PIN photodiodes [24], electrodes [25] and magnetoresistive sensors [26]. Approaches such as label-free electrical impedance-based ones [27,28], while quantitative and high throughput, present high sensitivity to the sample matrix, being affected by components in the sample other than the target, specifically their charges, which greatly hinders these devices' use in off-laboratory locations. This could also occur in fluorescent applications [29], due to non-specific adsorption of fluorophores or self-fluorescence of sample components [30]. Some platforms present a static detection [31] where labels complementary to the target are immobilized on sensor's surface. This approach, while sensitive and quantitative, is limited by the sensor's surface area and number of immobilized labels/targets, further requiring careful selection of sample flow rate.
Other sample focusing techniques have also been applied [19,20,22,32]. Along with detection and enumeration, high speed sorting has been an important theme in the development of microfabricated flow cytometers and for other applications [33][34][35][36][37][38]. Several strategies have been applied to this effect [3,19,[39][40][41][42][43][44]. Regardless of the use of solid-state devices to reduce the volume of the whole system, these microfabricated systems require external equipment for the detection and enumeration of cells/particles [30].
This work describes the development, characterization and application of a magnetic detection device for the identification and quantification of Streptococcus agalactiae in a complex matrix (milk), based on a previously developed platform [26,30], schematically depicted in Figure 1. This device comprises magnetoresistive (MR) sensors, namely spin-valve sensors (SVs), integrated with a microfluidic platform and connected to an amplification and acquisition setup. The sensors have excellent spatial resolution (on the micrometer range) and are sensitive to the magnetic field created by magnetized beads flowing in microchannels above the sensors. The detection scheme used in this platform relies on the MR sensor's sensitivity to count individual cells in flow (contrary to other approaches [45]) while providing information on nanoparticles' magnetization direction along the flow process. Therefore, no additional cell culture is needed. In addition, this platform is compatible with complex matrixes without the need of intricate sample pre-processing, while using a detection principle (magnetic) non-existent in Nature (thus greatly reducing biological background noise and false positives). The use of magnetoresistive sensors also simplifies connection with electrical equipment while still allowing coupling with other detection techniques (e.g., fluorescence or a laser-irradiated magnetic sead system (LIMBS)) if needed. This work, unlike other platforms [46][47][48] provides a simple approach for single cell detection, without the need for cell guiding mechanisms of hydrodynamic focusing approaches, targeting at a broader area of application-such as bacterial detection in food and water samples. To be competitive to other bulk cytometers, however, this approach should be able to accept large volumes of samples, operating with high flowrates (cm/s at least), and using multiple channels in a parallel sampling architecture.

Sensor Design and Detection Scheme
Spin-valves (SV) are magnetoresistive sensors [49,50] composed of a non-magnetic (NM) metal between two layers of ferromagnetic (FM) metals, one of which (the pinned layer) has its magnetization fixed by an adjacent antiferromagnetic layer, while the other (the free layer) is free to rotate ( Figure 2). Under an external magnetic field, it is possible to switch the relative magnetic orientations of the FM layers from parallel to antiparallel, therefore changing the sensor resistance, linearly with the cosine of the relative angle between the pinned layer and the free layers θ − θ , according to Equation (1), where ΔV is the variation in potential obtained at electric current I due to sensor's resistance change. Here, MR is the sensor magnetoresistance ratio (Equation (2)) and 〈cos θ − θ 〉 is averaged over the active area of the sensor (between contacts): where, ∎ is the square resistance of the sensor, the length of the sensor, the width of the sensor, the current applied to the sensor, the resistance of the sensor when θ − θ = π and the resistance of the sensor when θ − θ = 0.
To achieve a linear behavior (2), the free and pinned layer easy axes should be orthogonal. The linearization can be obtained by inducing an orthogonal magnetization direction between pinned and free layers during deposition or by patterning the SV with a large aspect ratio so that orthogonal magnetizations are facilitated by the demagnetizing fields [26,30,51]. The dimensions of the SV sensors are optimized taking into account final sensor application. In biological applications, the detection targets have sizes ranging between few nm (molecules such as DNA, RNA and various proteins) to tens of µm (cells can vary in size from 1 µm, like the target cell described in this paper, to 100 µm, size of a big plant eukaryotic cell). Detection is performed through magnetic labeling of these biological targets with nano-or micrometer superparamagnetic particles, which under a magnetic field acquire a magnetic moment. This creates a fringe field that can be detected by the sensor, through a change in its resistance. Using Equation (1), if one considers a coherent magnetization model for the free layer rotation, then: where is the external field, is the bias field used to center the SV transfer curve, is the sum of the ferromagnetic Néel coupling between the free and pinned layers, is the free layer anisotropy field and is the demagnetizing field.
Therefore the sensor output (Equation (1)) can be written as Equation (4) below: where = is the sensitivity of the sensor, = ∎ × is the resistance of the sensor when θ − θ = 0.
Here, represents the external magnetic field, averaged over the sensor area. In our case this is the fringe field created by the magnetic labels [30,52,53]. The dynamical detection approach employed in this work involves the application of a magnetic field perpendicular to the sensors in order to magnetize the beads labeling the cells, with minimum impact on the in-plane sensing direction of the sensor (as will be described in Section 3.4). The dynamic detection mechanism is illustrated in Figure 3, where a vertically magnetized particle is injected through a microchannel and generates a variable field over the sensor. In position 1, because the large distance to the sensor, the fringe field produced by the particles is neglegible. As the particle approaches the sensor, the free layer will sense the right-side component of the particles fringe field, which changes the sensor resistance (position 2). When the particle is in the center of the sensor, the average fringe field of the particle is equal to zero vanishing the signal (position 3). Finally, as the particle passes the sensor (position 4), the free layer magnetization is affected by an opposite opposite fringe field component when compared to position 2. When the cells go away, the signal goes back to zero since no fringe field is sensed (position 5). As a result, a bipolar peak is the signature of the passage of a perpendicularly magnetized particle over the SV sensor.
In a dynamical approach, sample acquisition velocity depends on the electronics, thus allowing a high throughput and direct number of cells to number of signals relation. A dynamical acquisition requires magnetic labels with a high magnetic moment under an applied external magnetic field in order to obtain a large detectable fringe field, significantly larger than the noise background level. However, label selection needs to be carefully done, as these should possess a non-remanent moment in order to avoid particle clustering during the labeling process of the cells, which can result in cell clustering originating an underestimation on the cell counting. A reduced label size is also important in order to avoid the detection of isolated particles [30,51].

Experimental Methods
One general differentiation of the Streptococci is the Lancefield groups based on serological grouping determined by the antigen C-substance that is a group-specific cell wall polysaccharide. Streptococcus agalactiae belongs to Lancefield Group B. Before using the selected rabbit polyclonal antibody (pAb) anti-Lancefield Group B Streptococci (8435-2000 AbDSerotec) against Streptococcus agalactiae cells, we have done several western blotting (WB) trials and ELISA tests for specificity and sensibility confirmation.
As known, western blotting identifies with specific antibodies proteins that have been separated from one another according to their size by gel electrophoresis. The blot is a membrane, almost always of nitrocellulose or polyvinylidene fluoride (PVDF). In our case, we have used hydrophobic PVDF membranes because it exhibits better binding efficiency of blotted proteins and have high sensitivity. Then, the gel is placed next to the membrane and application of an electrical current induces the proteins in the gel to move to the membrane where they adhere. The membrane is then a replica of the gel's protein pattern, and is subsequently stained with an antibody.
The proteins used in our WB trials were cell wall proteins of bovine field isolates of Streptococci species (agalactiae (Lancefield Group B), uberis (ungroupable) and dysgalactiae (Lancefield Group C)) and also from bacteria standards (as CECT 183 for Streptococcus agalactiae and CECT 994 for Streptococcus uberis). Results proved specificity of this pAb at 2 µg/mL concentration for 3 h of incubation at RT), to Streptococcus agalactiae cell wall proteins. Blots were probed with 0.2 μg/mL of goat anti-rabbit antibody conjugated to horseradish peroxidase (HRP) (STAR 124P, AbDSerotec) after one incubation hour at RT. Consequently, we have obtained stained immunogenic proteins of molecular weight higher than 100 kDa (and also two distinct immunogenic proteins around 30 and 41 kDa of molecular weight belonging to Streptococcus uberis).
The enzyme-linked immune sorbent assay (ELISA) is a commonly used technique to detect antibodies or antigens in samples using the specific reaction of antibodies to their antigens [54]. For pAb sensibility quantification, we have performed ELISA tests. We have used standard and field isolates of bacterial cells suspensions (agalactiae, uberis and dysgalactiae) and once more, specificity was evidenced and sensitivity obtained: the minimal pAb concentration of 1 μg/mL still identifies 10 3 CFU/μL of Streptococcus agalactiae cells (and also 10 3 CFU/μL of Streptococcus uberis).
In this work, magnetic particles are used as labels of polyclonal antibodies anti-Group B Streptococci (probes) which are going to recognize (via biomolecular recognition) bacterial Streptococcus agalactiae cells (targets) in the sample. Biological affinities between nanobead surface protein A, IgG Fc fraction and, the antibodies and Lancefield Group B Streptococci cell wall immunogenic proteins are illustrated in Figure 4. After labeling, the cells are introduced in a microfluidic channel and the SV sensor detects the fringe field of the magnetic labels bound around the target cell. The sensors and microfluidic channels were microfabricated and optimized at INESC-MN for this application, using an acquisition setup adapted from a previous work [26]. The tests using raw milk in microchannels were carried out at INESC-MN. The cell culture and magnetic functionalization and labeling protocols were performed at CIISA, according to manufacturer protocols.

Beads Functionalization
Nanomag ® -D-spio 50 nm particles (79-20-501, Micromod Partikeltechnologie GmbH, Rostock, Germany) were selected because they have protein A at surface and can bind up to five IgG. The calculation of beads number and the amount of pAb anti-Group B Streptococci (8435-2000 AbD Serotec, Kidlington, UK) was based on the Streptococcus agalactiae (CECT 183) concentration in samples and considering 400 times more beads than the estimation for cell surface area saturation (1600 particles/cell calculated based on bead and cell surface areas) with 50 nm nanoparticles. The nanoparticles number for cell surface saturation (1600) was estimated according to Wolfram Mathematica 7.0 calculations. A magnetic particles concentration of 6.4 × 10 9 particles/µL was prepared for each sample in order to ensure the cells full coverage.

Bacterial Cells Magnetic Labeling
Streptococcus agalactiae cells (CECT 183) were grown at 37 °C overnight on blood agar plates and resuspended in Tripticasein Soya Broth (TSB) over 24 h at 37 °C ( [55] adapted protocol). After cell pellet collection through 2700 rpm centrifugation at 17 °C during 15 min. (HERMLE Z 383K centrifuge, Wehingen, Germany) and discarding the supernatant, PBS 1X (pH 7.2) buffer was added to absorbance reading at 600 nm (DU-68 Spectrophotometer, Beckman, Pasadena, CA, US) and for CFU/mL estimation. For incubation of 200 μL of magnetic particles with pAb anti-Group B Streptococci, milk and PBS volumes were prepared for final samples amount of 600 µL, and bacteria concentration of 10 4 CFU/µL. Incubation was performed at RT for 50 min assisted with rolls plate agitation.

Milk Samples Preparation
Raw milk for experiments was collected aseptically from a healthy cow. As known, milk is a natural buffer with pH between 6.6 and 6.8 [56]. Conventional microbiological tests were performed accordingly with NMC (1999) [56] protocols, to confirm no bacterial growth. Briefly, a raw milk drop (10 μL loop) was smeared on a COS blood agar plate (43021, Biomerieux, Craponne, France) and a MacConkey agar plate (610028, Liofilchem Diagnostic, Roseto degli Abruzzi, Italy) made in the laboratory and both submitted to 37 °C during 48 h.
To achieve defatted milk samples, raw milk samples were frozen at −20 °C over 24 h and then unfrozen. During freezing, fat "cold agglutination" occurs forming a top layer of crystallized fat globules at milk surface. This layer was removed and milk underneath was used as "defatted" one.

MR Sensor Fabrication
The chips fabricated in this work comprised 4 sets of rectangular SVs disposed in a line. Each SV set includes seven sensors with 3 μm width, and length varying from 20 to 100 μm (measured between contact leads), according with Figure 5. Sensor geometry was optimized to promote a linear, hysteresis-free transfer curve upon patterning into micrometric dimensions (sensor dimensions and shape definition). Additionally to individual sensors, we have included also four SV sensors connected in series. These configurations were designed to cover the width of the microchannel to be included above the chip. The sensors were fabricated on a 150 mm-diameter silicon wafer passivated with a 50 nm-thick

Microfluidic Channels Fabrication
A channel geometry of four single parallel channels with 100 μm height, 100 μm width and 1 cm length was considered. The inlets have a tear shape to ease cell entrance in the channels. Channels were made of poly (dimethylsiloxane) (PDMS) and fabricated by cast-molding, following a procedure similar to [58]. A hard-mask used to expose channels' mold was made of Al 98.5 Si 1.0 Cu 0.5 150 nm thick layer deposited on Corning glass by PVD in a Nordiko 7000 tool, patterned by DWL lithography and chemically etched with a solution of acetic acid (3.3%), nitric acid (3.1%) and phosphoric acid (3.0%). Channels' mold was fabricated by contact microlithography (Figure 6) of a 100 μm thick SU-8 50 photosensitive negative resist spun onto a silicon wafer and it was observed for defects in a DEKTAK 3030ST profilometer. In order to cast PDMS channels with a controlled thickness and shape and inlet/outlet aligned with microchannel, three 2 mm-thick poly (methylmethacrylate) (PMMA) plates were micromachined with a CNC TAIG Micro Mill tool. The resist mold was mounted on the bottom plate with Kapton tape, then the middle plate defined PDMS (2 mm) shape and thickness and the top plate defined inlet and outlet holes (1 mm). The two top plates possess alignment holes (2 mm).

Bonding and Encapsulation
Silicon chip integration with PDMS microchannels was achieved through irreversible bonding of the Si 3 N 4 and PDMS surfaces by ultraviolet/ozone (UVO). Both surfaces were treated in a UVO Cleaner (Jelight, USA) for 15 min and immediately submerged in deionized water. Chip and microchannels were then mounted face-to-face, aligned on a microaligner with an x, y and theta direction stages under a microscope, using ethanol to delay bonding until complete alignment (Figure 5b). After drying at room temperature, the bonded device was placed in an oven at 70 °C for 30 min to complete irreversible bonding. Encapsulation was performed by mounting and gluing the bonded device on a Printed Circuit Board (PCB). The connection to the microfabricated sensors was done by wire-bonding the contact pads with the PCB. Finally, the wires were covered with silicone gel for protection. Figure 7 shows the final assembled device.

Samples Measurement
Sensor output signals were obtained using the electrical scheme shown in Figure 8. A 3 mA current was supplied to the sensor by two 9 V batteries in series (~18 V), 1 kΩ resistance (R R ), a potentiometer (R pot ) set at 5 kΩ (R R and R pot together have a higher resistance than the sensor's average resistance, R S ~555 Ω). The output of the sensor was connected to acquisition setup composed by (a) an amplifier (Stanford Research Systems SR560, California, US) operating for gains of 10,000x, (b) high-pass and low-pass filters of 300 (to filter the DC and part of low frequency noise) and 10,000 Hz (to avoid aliaising), respectively and (c) a 16 bit analogue to digital converter (ADC) board DT9836-12-2-BNC (20 kHz acquisition frequency), which was connected to a laptop, where a home-made software was used to acquire sensor output vs. time (Figure 8b,c). Each test required channel inlet sample introduction through capillary tubes (BTPE-90 polyethylene tubes, Instech Laboratories, Inc., Pennsylvania, PA, USA) plugged into a 1 mL syringe (Codan, Cat: 621640, LuerStubs LS20, Pennsylvania, PA, USA). Fluid flow was controlled by an automated syringe pump (NE-300 model, New Era Pump Systems, Inc., New York, NY, USA), and the sample was collected from the outlet by another capillary tube to a disposable Eppendorf.

Results and Discussion
In order to be able to understand experimental data, it is important to perform simulations of the signals varying the positioning of cells and relative positioning of cells inside the channel. It is also important to understand the variations of the signals for different directions of the magnetic moment.

Sensors Characterization and Magnet Calibration
The sensors' transfer curve (resistance versus DC magnetic field up to 140 Oe) was characterized. Figure 9 shows a representative example of a 100 × 3 μm 2 sensor, showing a linear range of 65 Oe, a sensitivity (S) of 0.24%/Oe, offset field ( ) of −0.35 Oe and coercivity ( ) of 0.40 Oe. A summary of the dispersion obtained over the 28 sensors measured on the chips fabricated is depicted in Table 1. From the table, one can confirm that connecting the sensors in series reduces the dispersion in all parameters because it averages the individual SV characteristics.  To set a vertical magnetic field to magnetize the nanoparticles, a permanent magnet block (dimensions 20 × 20 × 3 mm 3 , NdFeB, Supermagnete, Gottmadingen, Germany) with ~6.29 × 10 4 A/m was mounted below the PCB with the bonded device. As the SV are only sensitive to an in-plane, if well aligned, the magnet will not affect the sensitivity of the sensor. However, a small tilting of the magnet can create magnetic field components in the sensor plane and therefore affect the sensor's sensitivity. The impact of the magnet positioning on the sensor transfer curve is illustrated in Figure 10. With a well aligned magnet, the sensor transfer curve is centered around zero external fields, with maximum sensitivity (Figure 10a). A slight tilt of the magnet creates fields in the longitudinal (Figure 10b) and/or sensing direction (Figure 10c) that shift the sensor transfer curve and/or decrease the sensor sensitivity, respectively. Therefore, combining magnet positioning and transfer curve measurements, it was possible to achieve a magnet position that did not degrade the sensor sensitivity (Figure 10a).

Sensors Magnetic Behavior, Signal Amplitude and Shape
In this work, several sensor dimensions were tested in order to ascertain the best configuration for dynamic single cell detection in a wide range of concentrations in terms of signal intensity and noise level (signal-to-noise ratio). Three different sensors were fabricated: four 20 × 3 µm 2 sensors in series, a sensor with a large detection area (100 × 3 µm 2 ) and a sensor with a small area (20 × 3 µm 2 ). Both sensors in series and the large area sensor allow increasing the detection area available with a higher signal intensity, however are probably unable to distinguish between two or more cells flowing over the sensor simultaneously. In terms of magnetic labels, the chosen 50 nm superparamagnetic particles have a very low individual moment (~2.7 × 10 −18 Am 2 ) and so cannot be individually detected, while having a magnetic moment sufficient for detection of a completely covered cell. Their small dimension reduces the probability of several cells to bind to a single particle causing cell aggregation.

Magnetic Detection Simulations
In order to assess the expected signal that would be obtained with each sensor dimension, a simulation of their sensing behavior and sensitivity to magnetic labeled cells was performed in Wolfram Mathematica 7.0. To simulate the magnetically labeled cells' behavior over sensor's free layer we considered that each particle labeling the cell could be approximated to a magnetic dipole centered on the geometrical center of the particle, which was considered a sphere ( Figure 11). Figure 11. Out-of-plane magnetic field application for magnetic label sensing measurement schematics.
Dipole's magnetic field created at the position from dipole center is given by Equation (5): As SVs used in this work are only sensitive to transverse in-plane component of beads' fringe field, only the magnetic field parallel to sensor plane (x-component of fringe field) for particles with a magnetic moment ( ) was calculated. The field below a particle at position ( , , a) when it is magnetized perpendicularly to the sensor plane (z direction), is thus given by: As SVs are sensitive to the average fringe field generated by magnetically labeled cell, field of each particle has to be averaged over the sensor, which can be achieved by integrating Equation (6) over the sensor area and divide the result by the area of the sensor. In the specific case of this work, the fringe field of a cell ( ) with 1 µm diameter fully covered with 50 nm superparamagnetic particles (1600 nanoparticles per cell) was calculated considering spherical cell and each particle occupying a circle area equivalent. Since each Streptococcus agalactiae cell is composed by pairs of 1 μm coccus, simulations were performed for pairs of spheres. The simulation was performed considering the geometry presented in Figure 12 with axis positioned in each sensors center and assuming sensors positioned in the center of the channel. In this simulation, the fringe field generated by a single particle was calculated considering its position on the cell (Equation (7), where is the cell's radius, is the angle within the x/z plane and φ is the angle within the x/y plane) (Figure 13), which was assumed as a pair of spheres flowing in the middle of the channel (Figure 14): = × cos φ Cells' fringe field was obtained for 62 different positions of (0.8 μm apart covering a range from −25 μm and 25 μm in the x axis, considered the flow direction) and plotted against its corresponding position relative to sensor's center.
In order to obtain simulated signals similar to experimental ones, cells' fringe field was transformed into electrical signal (ΔV) using Equation (4), considering = , = = 0 , = 3 mA and the sensors characteristics of Figure 14. Initially, signal simulations were performed considering one or two cell pairs oriented according to one of the axis ( Figure 14) and at different height above the sensor. In accord with simulations, different signal shapes and intensities were observed if the cell pair is oriented along x, y or z axis. In Figure 15a, when compared to the x and z orientation, an intensity increase of 200 μV was observed for y axis orientation. This is due to the fact that pair of cells was aligned along the length of the sensor and therefore the observed signal corresponds to two 1 µm cells. This is not the case for the other two orientations since in one case, the 1 µm cells are flowing one after the other (x orientation) and, in the other case, one of the 1 µm cells is more apart from the sensor leading to a signal attenuation. Nevertheless, these signals shape remain very similar and for simplification, only the pair of cells aligned in the x direction will be further analyzed. In Figure 15b, the signal variation with channel height for a cell pair is shown. It can be observed that the higher the cell flows along the channel the lower is the signal intensity. The amplitude of the bipolar peak above a height of 10 µm is below 15 µV. Since the sensor noise is 10-15 µV [30], we can conclude that cells passing at a height above 10 µm are unable to be detected. Besides the specific cells presence detection, the final goal of this device will be also to set known cells concentration in samples. It is therefore important to understand which signal shapes are obtained when two pairs of cells are flowing one after the other, side by side or one on top of the other. Figure 16b shows the signal of two pairs of cells flowing one after the other over the sensor with a separation of 2 and 10 μm. For a cell pair distance of 2 μm, a bipolar signal is observed with a slight slope variation near zero and no variation in the amplitude when compared to a single pair of cells. For a distance of above 10 μm between each pair, the signal of each cell is already decoupled and two bipolar peaks are observed. These signatures permit the identification of two pairs of cells passage one after the other. Furthermore, by increasing the height of the cells, as expected, the signal amplitude is reduced (Figure 16c). As discussed previously, for heights above 10 μm, the signal is below the expected sensor noise and therefore the sensors are unable to detect it. For cells flowing simultaneously above the sensor at different heights ( Figure 17) similar peaks are observed. As can be observed in Figure 17a, for separations larger than 30 µm, there is an attenuation of 200 µV. This is due to the fact that in this case only the cell closer to the sensor generates a fringe field high enough to be detectable. Again, as expected, when the two pairs of cells (2 µm separated from each other) flow at a height above 10 µm, the signal is within the sensor's noise enabling it to detect the cells.  (Figure 18a) is observed. As sensor is 100 µm long, which corresponds to channel's width, the signal will remain independent of cells separation distance in y direction. As in previous cases, the amplitude of bipolar peaks will be attenuated as cells flow more apart from sensors. Sometimes, in real data it is not easy to distinguish if two cells are flowing side by side in y direction, since a large signal can correspond to several cells flowing simultaneously at a specific height or to a single cell pair flowing at a lower height. This fact would lead to an underestimation of detected cells number. To avoid this, one can spread several small sensors over the channel's width and measure them in parallel. Assuming that one of these sensors would have an area of 20 × 3 µm 2 , simulations were carried out for two pairs of cells separated by 2 and 10 µm in y axis. As expected from Equation (4), a lower signal is obtained for these sensors than for 100 × 3 µm 2 sensor in the same conditions ( Figure 19). Plus, due to sensor's smaller length than the channel, there is a decrease in signal detection outside sensor area due to dilution of cell magnetic fringe field with distance. Thus, cell pairs flowing outside 20 × 3 µm 2 sensor's sensitive/detection will not be observed. Spreading the small sensors would therefore give more reliable results than using a single sensor occupying the channel's width. However, from the electronics point of view, a more complicated system would be required, to allow acquisition of several sensors in parallel. Finally, it has previously been observed that cell rotation due to flow or non-vertical magnetization direction influences signal peak shape observed [26]. Thus, the influence of a magnetic moment component in the x direction ( Figure 20) was analyzed by introducing Equations (9) and (10) in the simulation: where is the saturation magnetic moment of the particles and for the 50 nm particles used in this work = 2.7 × 10 Am 2 . Figure 21 presents signal variation with angle β between perpendicular and parallel magnetization directions. It is possible to observe that the introduction of parallel component of fringe field is translated as an asymmetric bipolar signal, which intensity decreases with higher β, since the contribution of perpendicular component decreases. When the magnetization direction is parallel to x axis a unipolar peak is observed.

Detection of Streptococcus Agalactiae Cells
For a first experiment, three samples were considered: (i) raw milk as collected; (ii) milk spiked with 200 µL of functionalized nanoparticles (6.4 × 10 9 particles/µL for 10 4 cells/µL detection) (after MACS separation of buffer solution with 145 µL of magnetic nanoparticles and 10.6 µL of pAb anti-GB Streptococci, per sample); (iii) Similar to (ii) and also spiked with 4.6 µL of Streptococcus agalactiae cells suspension (1.28 × 10 6 cells/µL). The samples were first incubated for 50 min and injected inside the microfluidic channel at a 50 µL/min flow rate. Results presented were obtained from repeated trials (three times for each sample) and with at least ten measurements for each flow rate.  Figure 22a shows that for the sample (i), no peak was observed and only the background noise of the sensor was observed. A peak-to-peak noise of 20 µV was measured. On the other hand, on sample (ii), where only functionalized magnetic particles were mixed with the raw milk, sporadically small peaks (<40 µV) appeared (Figure 22b). This may be explained by formation of small particles agglomerates due to applied magnetic field or due to adsorption of particles to milk constituents. From this point, all peaks above 40 µV of peak-to-peak amplitude were considered as positive bacterium detection peak. In fact, on samples (iii) including magnetically labeled Streptococcus agalactiae cells, large peaks (~ 325 µV) were observed proving a positive detection of cells. As can be observed in Figure 22c, the measured peaks were unipolar. This indicates that magnetic moment of magnetic particles was almost oriented in the x direction (β~π/10). This seems contradictory since a vertical magnetic field is applied during the experiment. In fact, this phenomenon was already observed in the past for magnetic particles [26] in a similar system and was associated to rolling of the magnetic particles over the sensor surface. However, this was the first time that such behavior was observed in cells.
Analyzing the literature, several works reported that in square or rectangular channels cells tend to focus on four equilibrium regions centered at channel edge as shown in Figure 23 [59,60]. This is valid for dilute suspensions of particles or cells flowing at moderate Reynolds numbers [59]. Considering that in this work, cells are magnetically label and the magnet is positioned bellow sensors, there may be a magnetic force pushing cells downwards. This small magnetic force associated to the equilibrium regions will therefore pull cells towards sensor surface. The surface drag force associated to the parabolic liquid velocity will further cause rolling of cells over the sensor.
On other trials, more symmetric peaks were observed ( Figure 24) and with a lower amplitude. This demonstrates that in some cases cells flow above the sensor without rolling over the surface. When compared to the simulations of Section 4.1 (Figure 15b), peaks amplitude of 111 µV and 143 µV indicate cells positioning of 3-5 µm above sensor. Furthermore, peaks observed in Figure 24a are superimposed showing that at least two cells are flowing very close to each other but still far enough to distinguish two bipolar peaks. Comparing to simulations (Figure 16b) this indicates that cells are flowing one after the other at a separation ranging from 5 to 10 µm. This fact may be explained by some cell agglomeration at the inlet and consequent sporadic magnetic labeled cells release, generating peaks with these characteristics.
Based on the peak analysis, it is possible to estimate the concentration of bacteria in solution. Assuming an average of four peaks per acquisition of 30 s, a flow rate of 50 µL/min (meaning 25 µL of sample volume) and that each peak corresponds to a single cell, a bacteria concentration of 0.14 cells/µL is obtained. This value is far below the concentration of the input sample (10 4 CFU/µL). This fact reinforces the assumption that there is a strong agglomeration of cells at channel's inlet and that only few cells are released into the channel and measured. This agglomeration could be explained by the large vertical (z direction) magnetic gradients created by magnet at channel's inlet. Some cells could only be released by the Stoke force created by a large flow rate (50 μL/min). Therefore, further optimizations of the system could include new inlet geometry, more homogeneous external magnetic field and/or higher flow rates, aiming at reduction of magnetic agglomeration at the inlet.
The platform's quantitative detection limit still requires further testing, however a limit of 10 CFU/µL has already been attained in a yes/no answer format as described in our subsequent work [61].
A quantitative output of the platform can be obtained, through correlation between experimental and simulated peaks. One example can be seen in Figure 25, where one experimental peak from raw milk with bacteria could be compared with the simulation assuming a −π/10 rotation (in x direction).
The amplitude of 60 µV was achieved by considering that two agalactiae cells were contributing to the detected signal.  Finally, a study comparing measurements in raw milk and defatted milk samples was performed. As observed in Figure 26b,c, the peaks' shape in defatted and raw milk are very similar and, as discussed previously, indicate that cells are rolling over sensor's surface. However, peaks in defatted milk show almost twice the amplitude than peaks in raw milk. This can be explained by the fact that raw milk is a more complex solution than defatted milk and therefore its constituents (fat globules, casein, etc. can be blocking the binding of magnetic nanoparticles to bacteria. This fact leads to a lower load of magnetic nanoparticles on bacteria inside raw milk and thus lower signals are measured. On the other hand, PBS solution presents smaller amplitude peaks than milk samples (Figure 26a). Meaning probably that bacterial cells have less magnetic particles bonded. It is coherent with its simpler saline composition, unlike milk complexity, explaining more individualized bacteria cells. However, milk samples larger peaks show us that sensor might detect bacteria cells agglomerations due to favorable immunological bonding conditions existing in milk.

Conclusions/Outlook
This work describes a platform for in-flow detection of magnetically labelled cells with a magnetoresistive based cell cytometer, as an inexpensive and portable alternative to current flow cytometry in bio-applications. Sensor output response to cells and particles were simulated to obtain the impact of their position in the microchannel with respect to the sensor. Simulations indicated that the analysis of the different peaks' amplitudes and shapes can infer the position of the cells inside the channel and eventual simultaneous passage of two cells over the sensor. Experiments performed in raw milk, defatted milk and PBS buffer demonstrated specific detection of Streptococcus agalactiae cells.
The results indicate that raw milk constituents (fat globules, casein, etc.) inhibit the bonding of nanoparticles to the bacteria leading to lower signal amplitudes. On the other hand, quantification is still an output to be improved since the cells seem to agglomerate at channel's inlet due to a strong magnetic gradient. To overcome this limitation, a different inlet/channel design, more homogeneous external magnetic field and/or higher flow rates could reduce this cells agglomeration deliver more reliable quantification values.

Author Contributions
Ana C. Fernandes was responsible for acquisition setup assembly and simulations. Carla M. Duarte was responsible for sensor characterization, magnetic particles functionalization, Streptococcus agalactiae microbiological manipulation and milk samples trials. Filipe A. Cardoso gave support both in acquisition setup assembly and results interpretation. Ricardo Bexiga kindly supplied innocuous milk samples. Susana Cardoso optimized the spinvalve sensors and Paulo P. Freitas helped in results interpretation and paper revision.