Optical fingerprints for label-free high-throughput (epi)genomics, transcriptomics, and proteomics profiling of single cells. Vibrational spectroscopy signatures combined with a molecular identification algorithm rooted in machine learning enables identification of nucleic acids and amino acids, and their molecular variations, thereby identifying genetic variation by mapping heterogeneity and identifying low copy-number variants. Additional embodiments include the BOCS algorithm which takes measurements of DNA k-mer content from high-throughput single-molecule Raman spectroscopy measurements and maps them to gene databases for probabilistic determination of genetic biomarkers at low coverages. Starting with a log of measured k-mer content blocks (B1 . . . Bn as shown) and a genetic biomarker database (excerpts from the MEGARes antibiotic resistance database are shown), the blocks are individually aligned to each gene in the database based on content. This alignment consists of finding all match locations for the k-mer block content within a gene via translating through the gene one nucleotide at a time and looking at fragments of length k. For each block, a raw probability can be calculated for each gene based on the number of matches for the k-mer block content within the gene, length of the k-mer block, and length of the gene (calculation shown in the schematic). As more blocks are analyzed, probabilities are compounded and genes in the database are ranked. The gene(s) from which the Raman-analyzed k-mer blocks originate quickly generate the top probabilities and can often be determined in coverages <<1.0, meaning that only a small fraction of the gene blocks need to be analyzed for identification of a specific genetic biomarker.
SINGLE-MOLECULE OPTICAL SEQUENCE IDENTIFICATION OF NUCLEIC ACIDS AND AMINO ACIDS FOR COMBINED SINGLE-CELL OMICS AND BLOCK OPTICAL CONTENT SCORING (BOCS): DNA K-MER CONTENT AND SCORING FOR RAPID GENETIC BIOMARKER IDENTIFICATION AT LOW COVERAGE