Sequencing-guided re-estimation and promotion of cultivability for environmental bacteria
While culture-independent approaches made significant strides in characterizing microbial evolution and ecology, isolates remain indispensable for robust functional studies and reliable application – . Nonetheless, the enigma known as “the great plate count anomaly” persisted for over a century . The disparity between the total cell count, typically determined through microscopy or flow cytometry, and the colony-forming units (CFUs) on agar plates differed by several orders of magnitude in typical environmental samples , , , . Similar or higher proportions of cultivable cells were obtained for liquid media using the most probable number counting approach – . As a result, a substantial proportion of environmental prokaryotes was considered uncultured or uncultivable in the laboratory, including those referred to as “microbial dark matter” or candidate divisions, which were lineages lacking any representative isolates at high taxonomic ranks , . A prevailing misconception is the confusion between the proportion of cultivable cells (PCC) and the proportion of cultivable taxa (PCT). Nearly all previous studies have measured PCC , – rather than focusing on the proportion of cultivable taxa (e.g., species) relative to the total taxa present in the inoculum. To accurately assess cultivable diversity, it is imperative to utilize PCT, as proposed by Martiny , . Martiny also conducted a PCT estimation based on a 16S rRNA gene database, raising concerns about the potential underestimation of cultivability in terms of taxa. However, a subsequent discussion argued that this conclusion was based on an inherently biased concept . It further emphasized that it is impossible to determine the cultivability of a microbe until successful cultivation is achieved under certain conditions. The primary factors contributing to the low cultivability of environmental microorganisms have been extensively documented, and included unfavorable conditions (e.g., non-optimal physiochemical conditions, the presence of reactive oxygen species, and the absence of certain growth factors), dormancy, the rarity of many species and other constraints , , – . To improve cultivability, various strategies have been employed, such as in-situ cultivation, culturomics, and the use of innovative devices – . Among these, the culturomic approach stood out. It involved using easy-to-use agar plates under multiple cultivation conditions to broaden the cultivable diversity, along with rapid taxonomic classification methods (e.g., mass spectrometry and 16S rRNA sequencing ). This approach led to the discovery of numerous novel taxa from the human gut microbiome , . However, exhaustively picking and identifying all colonies is often impractical in terms of time and cost, particularly when dealing with hundreds or even thousands of agar plates across multiple samples and various cultivation conditions. Moreover, it has been reported that micro-colonies, invisible to the naked eye, could form during laboratory cultivation , . While these microcolonies were cultivable, they were likely to be overlooked during the isolation process. A cost-effective method of plate sequencing has been reported for taxonomically profiling all cultivable taxa growing on the agar plates and guiding isolation – . However, no novel taxa at high taxonomic ranks were obtained in these studies, possibly due to the limited range of samples and cultivation conditions examined. An alternative strategy for cultivating uncultured microbial taxa involves predicting their metabolic features based on their genomes , . This approach capitalizes on the wealth of genomic data obtained through culture-independent high-throughput sequencing methods. However, conventional metabolic reconstruction may be ineffective in guiding the isolation of taxa experimentally confirmed as uncultivable under standard conditions (i.e., not those selected by deliberately suppressing other cultivable taxa under stress-inducing conditions, such as the addition of antibiotics to the growth medium). To our knowledge, few successful cases of this strategy have been reported . We believe that the cultivable/uncultivable outcome under specific culture conditions can be treated as a phenotype, which can be linked to their genotypes through a genome mining strategy. Subsequently, these genotypes might provide insights into the essential cultivation conditions for these taxa. In this work, we set out to experimentally determine the PCT and PCC for two types of environmental samples: soil and activated sludge (AS), which contained both high cultivable and uncultivable bacterial diversity. We employed a simplified culturomic approach that emphasized the availability of oxygen (i.e., aerobic, anaerobic, and microaerophilic conditions, abbreviated as AE, AN and MI, respectively) and two types of culture media (common laboratory medium and extraction medium of the corresponding environment sample, the latter of which was demonstrated to be superior in environmental bacterial cultivation ) to profile the cultivable/uncultivable bacterial taxa, although not all conditions were tested for all samples (see Supplementary Data ). Cells grown on agar plates were harvested and subjected to high-throughput sequencing for taxonomic and genomic profiling. PCT was calculated as the ratio of detected amplicon sequence variants (ASVs, serving as taxonomic markers) from the agar plates to those from the inoculum (see Fig. for a schematic representation). The two major aims of this study were: (1) comparing PCC and PCT, thereby inferring potential biases in previous assessments of cultivability for environmental bacteria; (2) promoting the cultivability by referring to the bacterial cultivable phenotype. For the latter, two potential strategies were pursued: one involved comprehensive profiling of cultivable bacteria under specific culture conditions for targeted isolation of novel taxa at high taxonomic levels from specific samples; the other entailed leveraging comparative genomics between cultivable and uncultivable taxa within specific bacterial lineages to explore hints for improving culture conditions and enhancing the cultivation of uncultivable taxa. 

PCT is much higher than PCC Undoubtedly, during library construction and sequencing for amplicon samples, contamination ASVs , and cross-talk between samples could have effects on the definition of cultivable taxa. Therefore, it was necessary to assess their severity in our experimental settings. The impact of contamination was assessed as very low and related ASVs were excluded from downstream analyses (see Methods, Supplementary Fig. , and Supplementary Data ). Meanwhile, considering the special design of barcode-PCR (unique barcodes were used to multiplex individual samples within each library , as described in the Methods) and supporting evidence such as almost no library internal standard detection in experimental samples and no ASVs from abundant uncultured phyla of inoculum samples were detected in any cultivation sample (Supplementary Fig. ), it was inferred that cross-talk had a negligible effect on the results. Then, the PCT was calculated as follows: for all ASVs detected in the inoculum of a given sample, their detection proportion was determined for each agar plate (the plate-level PCT), across all plates under specific cultivation condition (the condition-level PCT), and across all plates of all conditions for that sample (the sample-level PCT). In contrast, the PCCs at the condition level and sample level were derived from the average values of each involved plate (the plate-level PCC), whereas the corresponding values for PCT were determined by the cumulative effect of ASV occurrences across all related plates. It is worth noting that a majority of detected cultivable ASVs (ranging from 43.8% to 83.5%, with a median of 72.8%) were not detected in the inoculum (Fig. ). This absence may primarily be attributed to their relatively low abundance in the corresponding inocula, which fell below the detection limits, as increasing sequencing depth can decrease these values ( P < 0.01, paired one-tailed t -test, Fig. ). Additionally, these ASVs exhibited significantly lower frequencies on the agar plates compared to other cultivable ASVs (Supplementary Fig. ). Moreover, most of these ASVs (74.1–94.7%) had phylogenetically related ASVs (with >94% similarity) in the inocula (Supplementary Fig. ). This evidence supports the idea that they were low-abundance taxa and co-occurred with other closely related phylotypes in the inoculum, rather than contaminants from unknown sources. Consequently, although these ASVs were not factored into the PCT calculation, they were classified as cultivable ASVs. As illustrated in Fig. , the PCT at the plate level showed a statistically significant difference from the PCC. For soil, the PCT was significantly higher than PCC with a mean difference of 2.62% ( P < 0.001, paired one-tailed t -test). For AS, however, their median values were comparable, yet they remained significantly different ( P < 0.001, paired one-tailed t -test). This variation could be linked to the relatively low (1.66% median) and high (4.76% median) PCC observed in soil and AS samples, respectively (Fig. ). The condition-level PCT was 0.6- to 39.3-fold (with a median of 3.1-fold) higher than the PCC ( P < 0.01, paired one-tailed t -test). At the sample level, PCT was 6.0- and 2.8-fold higher (median) than the corresponding PCC for soil and AS samples ( P < 0.01, paired single-tailed t -test), respectively (Fig. ). As the number of plates and cultivation conditions increased, the PCT gradually rose, with the extent to which it exceeded PCC increasing progressively. These results demonstrated that PCC can significantly underestimate the cultivable diversity of environmental bacteria. We also observed an increasing trend in PCT with the rise in sequencing depth from 10,000 to 20,000 reads (Fig. ). This phenomenon could be attributed to either the faster rate of ASV accumulation in cultivation samples compared to the inoculum (Supplementary Fig. ), or the detection of more cultivable ASVs in the inoculum (Fig. ) with increased sequencing depth. Furthermore, to explore whether we could estimate the proportion of cultivable bacteria at the sample-type level for soil and AS using a limited number of samples (five for AS and six for soil), we gradually increased the number of combined samples and observed whether the PCT values stabilized. As shown in Fig. , the PCT values showed a slight decreasing trend for both sample types. Therefore, estimating the cultivability of these two sample types cannot be reliably achieved based on a limited sample size. Factors affecting on cultivability We investigated the influence of cultivation conditions (including medium and the availability of oxygen), rareness (defined as the relative abundance of an ASV in the inoculum), and phylogenetic distance on the cultivability of ASVs. First, as depicted in Fig. , the use of extraction medium (directly using filtered in-situ wastewater and water extracts of soil as culture media) outperformed common laboratory medium (R2A and 1% strength tryptic soy agar) for both AS and soil samples, resulting in a higher number of cultivable ASVs ( P < 0.05, paired one-tailed t -test). Second, the availability of oxygen (AE vs. AN, excluding MI conditions from the analysis) significantly affected the cultivation ratio only for soil samples ( P < 0.05, paired one-tailed t -test, Fig. ). Third, as illustrated in Fig. , while higher cultivability ( P < 0.05, paired one-tailed t -test) was observed for dominant ASVs (>0.1%) compared to rare groups (≤0.1%), there was no significant difference between the two rare ASV groups with different relative abundance (0.01–0.1% vs. ≤0.01%, P > 0.05, paired one-tailed t -test). This suggests that rarity is not strongly correlated with lower cultivability for the rare biosphere. We compared the phylogenetic relatedness of cultivated and uncultivated ASVs with 16S rRNA gene databases of the type strains (RDP taxonomic training set version No. 19, excluding sequences from non-type strains). As expected, there was significantly higher similarity between cultivated taxa and the type strains ( P < 0.0001, Wilcoxon test, Supplementary Fig. ). This suggests that, in general, easy-to-culture taxa and their phylogenetic relatives are more likely to be cultured and described. However, it does not simply imply that using the same cultivation conditions reliably cultivates strains that are phylogenetically close to each other within samples. To test this, we assessed the probability of ASVs with varying phylogenetic distances within operational taxonomic units (OTUs) being classified as cultivable (requiring ≥ 5 ASVs and at least one ASV is cultivable, see Fig. ). Within 97%-level OTUs, the intra-OTU cultivation ratio was below 25% (median value) in both sample types. The ratios for 94%, 90%, and 85% OTU-levels gradually declined. These results indicate that phylogenetic distance might not be a reliable predictor of cultivability under certain cultivation conditions, even for closely related taxa. Substantial cultivable taxa are novel but rare on plates The amplicon data and metagenome-assembled genomes (MAGs) derived from cultivation samples allowed us to investigate the taxonomic novelty of the cultivable taxa. To assess the novelty of cultivable ASVs, we employed four thresholds: ≥ 98%, 94–98%, 90–94%, and <90% sequence similarity to any described type strains, as defined by the RDP database . In AS samples, the numbers (percentages) of cultivable ASVs corresponding to these thresholds were: 1201 (59.0%) for ≥ 98%, 658 (32.4%) for 94–98%, 139 (6.8%) for 90–94%, and 36 (1.8%) for <90%. In soil samples, the numbers were 1354 (56.0%) for ≥ 98%, 821 (33.9%) for 94–98%, 167 (6.9%) for 90–94%, and 77 (3.2%) for <90% (Fig. ). These results indicate that a substantial proportion of cultivable ASVs belong to undescribed taxa. The distribution of these proportions varies across different phyla. Dominant phyla, such as Pseudomonadota , Actinomycetota and Bacillota , have a relatively minor proportion of ASVs (<10%) with best-hit similarity to the database below 94%. However, in many rare phyla, such as Verrucomicrobiota , Acidobacteriota and Gemmatimonadetes in soil, the percentage of ASVs with <94% similarity ranges from 38.6 to 100%. This discrepancy could be attributed to the fact that formally described species within these rare phyla are much less abundant. Metagenomic sequencing for 17 selected agar plate samples produced 517 dereplicated MAGs across 11 phyla (Fig. , Supplementary Data , ). The GTDB-Tk classification revealed that the numbers of MAGs lacking annotation to a formally described taxon at the rank of class, order, family, genus, or species were 1, 3, 6, 65, and 294 (i.e., 56.9% of all MAGs were unannotated at the species level), respectively. This result suggests that metagenomic sequencing of the agar plates is an efficient method for obtaining taxonomically novel genomes. An analysis of the relative abundance of ASVs from cultivation samples revealed that ASVs with high novelty (<94% sequence similarity to the closest type strain) in both soil and AS samples collectively comprised only 0.04% and 0.06% (median value) of the total (Fig. ). This implies that novel taxa are generally present at very low abundance on the agar plates. These low-abundance ASVs remind us of the formation of microcolonies with high taxonomic novelty often observed on plates in previous literature , . Indeed, we identified unneglectable numbers of microcolonies (diameter <300 μm, approximately 20% relative to the number of macrocolonies, see Fig. and Supplementary Fig. ). By measuring the size of these colonies and randomly selecting some (43 microcolonies and 39 macrocolonies) for direct 16S rRNA gene amplification and sequencing, we found that microcolonies were more likely to be taxonomically novel (Fig. ). Similarly, grouping colonies by novelty revealed that those with similarity below 94% and those between 94-98% were significantly smaller than those with similarity above 98% ( P < 0.001, Wilcoxon test, Fig. ). However, the cultivation results indicated that most of these microcolonies failed to subculture on agar plates, obviously more so than the macrocolonies (Fig. ). This observation is consistent with a previous report of low sustainable growth for microcolonies . Sequencing-guided isolation of a strain representing a novel class Among all datasets, in the agar plate samples obtained from an enhanced biological phosphorus removal (EBPR) bioreactor, we identified a highly novel ASV and a MAG without 16S rRNA genes in the amplicon data and the metagenome, respectively (Figs. b and ). The ASV exhibited the closest similarity to Melioribacter roseus (in the phylum Chlorobiota ) with a sequence identity of 87.1%. In contrast, the MAG was classified under the c_ JABWAT01 class of Bacteroidota in the GTDB-Tk database. Notably, the ASV was exclusively detected on agar plates with extraction medium under anaerobic condition (Fig. ), where it reached very high relative abundance (>5%) on a few plates. By picking approximately 60 colonies, we obtained the isolate of the bacterium named as EBPR01. The isolate was found to contain the identical 16S rRNA gene sequence to the ASV and exhibited slow growth as a rod-shaped bacterium (Fig. ), requiring 5 to 7 days for small visible colony formation. Phylogenomic analysis confirmed that this bacterium represents the first isolate of the c_ JABWAT01 (Fig. ). Notably, all four reference genomes affiliated with c_ JABWAT01 originated from laboratory-scale nutrient removal bioreactors, including one also from an EBPR reactor (sp020161835, Fig. ). Therefore, it can be speculated that this lineage possesses specific functions within artificial systems of nutrient removal. General metabolic activity is found to be linked with cultivability As we could define the cultivable and uncultivable bacterial ASVs in inoculum, we designed an experiment to assess whether metabolic activity and short-term adaptivity after exposure to laboratory treatments were related to the cultivability for taxa of an AS sample (see Fig. and Methods ). Here, the metabolic activity of a given ASV was determined by measuring rRNA copies per cell, while adaptivity was quantified as the ratio of metabolic activity after laboratory treatment to that under in-situ conditions (details are provided in Methods ). Metabolic activity was measured under five conditions, including in-situ (O), after exposure to in-situ medium (I), R2A broth (R) for untreated bioflocs (UI and UR), or dispersed cells (DI and DR) in the laboratory for 1 hour. Meanwhile, we conducted cultivation on R2A agar plates for the corresponding ASVs to define their cultivability. Only qualified ASVs (31 cultivable and 48 uncultivable, see Methods ) were assessed for their metabolic activity. The results revealed that the metabolic activity of cultivable ASVs was significantly higher than that of uncultivable ones in all five conditions ( P < 0.001, Wilcoxon test, Fig. ). This suggests that general metabolic activity itself is associated with the laboratory cultivability of environmental bacteria. As shown in Fig. , relative to the in-situ condition (O), UI and UR treatments downregulated metabolic activity in uncultivable ASVs more than in cultivable ASVs ( P < 0.05, Wilcoxon test), while no difference was detected for DI and DR treatments, indicating that short-term adaptivity to laboratory treatments may not be strongly correlated with cultivability. When comparing exposure to in-situ conditions (UI/O and DI/O), both cultivable and uncultivable taxa showed an upregulation of metabolic activity after exposure to R2A broth (UR/O and DR/O), indicating that short-term exposure to artificial media may not impair their metabolic activity. Interestingly, regardless of cultivability, cell dispersion in in-situ medium (DI) led to a marked downregulation of metabolic activity (compared to UI), whereas dispersion in R2A conditions (DR) resulted in an upregulation of metabolic activity (compared to UR). This suggests that dispersion is detrimental to most taxa in in-situ medium (where these microbes form self-assemble aggregates), while it might be conducive to adaptation in artificial nutrient-rich media. Promoting cultivability by referring to phenotype-related genotypes For the MAGs from 6 AS inoculum samples and 12 corresponding agar plate samples (detailed in Supplementary Data ), we defined MAGs with cultivable and uncultivable phenotypes (see Methods ). This discrimination allowed us to perform comparative genomics to screen for genotypes associated with the phenotypes. To avoid potential false signals that might be enriched in different phylogenetic branches with uneven numbers of MAGs, it was crucial to conduct this analysis within a lineage where the two groups were phylogenetically intermixed. In our study, we focused on the order Burkholderiales , which contains many dominant and core taxa in global wastewater treatment systems , and comprises 111 cultivable and 29 uncultivable MAGs in our dataset (Supplementary Data ). Phylogenomic analysis showed that the genomes of the two groups were moderately mixed within this order (Supplementary Fig. ). Comparative genomic analysis unveiled a significant enrichment of certain clusters of orthologous genes (COGs) within the cultivable ( n = 66) and uncultivable ( n = 10) groups, respectively (Fig. and Supplementary Data ). While many of these genes did not exhibit clear functional associations with cultivability, we identified two potential features correlated with cultivability that were underrepresented in the uncultivable group. The first catalog includes six COGs associated with vitamin B 12 biosynthesis. Vitamin B 12 is considered a widely shared growth factor in microbial communities due to its complex biosynthetic pathway and extremely low demand, aligning with the ‘Black Queen hypothesis' . Notably, all but one examined Burkholderiales genomes in our study could be classified as vitamin B 12 -dependent, as per the definition by Shelton et al. . (Supplementary Fig. ). The second feature includes two COGs linked to oxidative stress tolerance (COG3022 and COG2717). To validate whether adding vitamin B 12 to R2A medium can enhance the cultivability of Burkholderiales , we compared cultivable bacteria in two additional AS samples (XM4 and ZZ1) under two conditions: with and without the addition of vitamin B 12 (5 μg/L). As depicted in Fig. , both samples from wastewater treatment plants displayed an increased number of detectable cultivable Burkholderiales ASVs ( P < 0.05, one-tailed t -test). In the case of the XM4 sample, the number of total cultivable ASVs also increased significantly ( P < 0.001, one-tailed t -test). Comparing the relative abundance of low-abundance Burkholderiales ASVs (<0.03% on average) detected on the control plates and their relative abundance in the treatment, both samples showed an increase of relative abundance of these low-abundance ASVs (potential slow growers) with the addition of vitamin B 12 ( P < 0.01, paired one-tailed t -test, Fig. ). These results suggest that the addition of vitamin B 12 enhanced the growth of these bacteria on the plates, thereby increasing the likelihood of their detection through amplicon sequencing. Therefore, these results validate that the differences in cultivable and non-cultivable genotypes obtained from comparative genomics can guide improvements in culture conditions. 

Undoubtedly, during library construction and sequencing for amplicon samples, contamination ASVs , and cross-talk between samples could have effects on the definition of cultivable taxa. Therefore, it was necessary to assess their severity in our experimental settings. The impact of contamination was assessed as very low and related ASVs were excluded from downstream analyses (see Methods, Supplementary Fig. , and Supplementary Data ). Meanwhile, considering the special design of barcode-PCR (unique barcodes were used to multiplex individual samples within each library , as described in the Methods) and supporting evidence such as almost no library internal standard detection in experimental samples and no ASVs from abundant uncultured phyla of inoculum samples were detected in any cultivation sample (Supplementary Fig. ), it was inferred that cross-talk had a negligible effect on the results. Then, the PCT was calculated as follows: for all ASVs detected in the inoculum of a given sample, their detection proportion was determined for each agar plate (the plate-level PCT), across all plates under specific cultivation condition (the condition-level PCT), and across all plates of all conditions for that sample (the sample-level PCT). In contrast, the PCCs at the condition level and sample level were derived from the average values of each involved plate (the plate-level PCC), whereas the corresponding values for PCT were determined by the cumulative effect of ASV occurrences across all related plates. It is worth noting that a majority of detected cultivable ASVs (ranging from 43.8% to 83.5%, with a median of 72.8%) were not detected in the inoculum (Fig. ). This absence may primarily be attributed to their relatively low abundance in the corresponding inocula, which fell below the detection limits, as increasing sequencing depth can decrease these values ( P < 0.01, paired one-tailed t -test, Fig. ). Additionally, these ASVs exhibited significantly lower frequencies on the agar plates compared to other cultivable ASVs (Supplementary Fig. ). Moreover, most of these ASVs (74.1–94.7%) had phylogenetically related ASVs (with >94% similarity) in the inocula (Supplementary Fig. ). This evidence supports the idea that they were low-abundance taxa and co-occurred with other closely related phylotypes in the inoculum, rather than contaminants from unknown sources. Consequently, although these ASVs were not factored into the PCT calculation, they were classified as cultivable ASVs. As illustrated in Fig. , the PCT at the plate level showed a statistically significant difference from the PCC. For soil, the PCT was significantly higher than PCC with a mean difference of 2.62% ( P < 0.001, paired one-tailed t -test). For AS, however, their median values were comparable, yet they remained significantly different ( P < 0.001, paired one-tailed t -test). This variation could be linked to the relatively low (1.66% median) and high (4.76% median) PCC observed in soil and AS samples, respectively (Fig. ). The condition-level PCT was 0.6- to 39.3-fold (with a median of 3.1-fold) higher than the PCC ( P < 0.01, paired one-tailed t -test). At the sample level, PCT was 6.0- and 2.8-fold higher (median) than the corresponding PCC for soil and AS samples ( P < 0.01, paired single-tailed t -test), respectively (Fig. ). As the number of plates and cultivation conditions increased, the PCT gradually rose, with the extent to which it exceeded PCC increasing progressively. These results demonstrated that PCC can significantly underestimate the cultivable diversity of environmental bacteria. We also observed an increasing trend in PCT with the rise in sequencing depth from 10,000 to 20,000 reads (Fig. ). This phenomenon could be attributed to either the faster rate of ASV accumulation in cultivation samples compared to the inoculum (Supplementary Fig. ), or the detection of more cultivable ASVs in the inoculum (Fig. ) with increased sequencing depth. Furthermore, to explore whether we could estimate the proportion of cultivable bacteria at the sample-type level for soil and AS using a limited number of samples (five for AS and six for soil), we gradually increased the number of combined samples and observed whether the PCT values stabilized. As shown in Fig. , the PCT values showed a slight decreasing trend for both sample types. Therefore, estimating the cultivability of these two sample types cannot be reliably achieved based on a limited sample size. 

We investigated the influence of cultivation conditions (including medium and the availability of oxygen), rareness (defined as the relative abundance of an ASV in the inoculum), and phylogenetic distance on the cultivability of ASVs. First, as depicted in Fig. , the use of extraction medium (directly using filtered in-situ wastewater and water extracts of soil as culture media) outperformed common laboratory medium (R2A and 1% strength tryptic soy agar) for both AS and soil samples, resulting in a higher number of cultivable ASVs ( P < 0.05, paired one-tailed t -test). Second, the availability of oxygen (AE vs. AN, excluding MI conditions from the analysis) significantly affected the cultivation ratio only for soil samples ( P < 0.05, paired one-tailed t -test, Fig. ). Third, as illustrated in Fig. , while higher cultivability ( P < 0.05, paired one-tailed t -test) was observed for dominant ASVs (>0.1%) compared to rare groups (≤0.1%), there was no significant difference between the two rare ASV groups with different relative abundance (0.01–0.1% vs. ≤0.01%, P > 0.05, paired one-tailed t -test). This suggests that rarity is not strongly correlated with lower cultivability for the rare biosphere. We compared the phylogenetic relatedness of cultivated and uncultivated ASVs with 16S rRNA gene databases of the type strains (RDP taxonomic training set version No. 19, excluding sequences from non-type strains). As expected, there was significantly higher similarity between cultivated taxa and the type strains ( P < 0.0001, Wilcoxon test, Supplementary Fig. ). This suggests that, in general, easy-to-culture taxa and their phylogenetic relatives are more likely to be cultured and described. However, it does not simply imply that using the same cultivation conditions reliably cultivates strains that are phylogenetically close to each other within samples. To test this, we assessed the probability of ASVs with varying phylogenetic distances within operational taxonomic units (OTUs) being classified as cultivable (requiring ≥ 5 ASVs and at least one ASV is cultivable, see Fig. ). Within 97%-level OTUs, the intra-OTU cultivation ratio was below 25% (median value) in both sample types. The ratios for 94%, 90%, and 85% OTU-levels gradually declined. These results indicate that phylogenetic distance might not be a reliable predictor of cultivability under certain cultivation conditions, even for closely related taxa. 

The amplicon data and metagenome-assembled genomes (MAGs) derived from cultivation samples allowed us to investigate the taxonomic novelty of the cultivable taxa. To assess the novelty of cultivable ASVs, we employed four thresholds: ≥ 98%, 94–98%, 90–94%, and <90% sequence similarity to any described type strains, as defined by the RDP database . In AS samples, the numbers (percentages) of cultivable ASVs corresponding to these thresholds were: 1201 (59.0%) for ≥ 98%, 658 (32.4%) for 94–98%, 139 (6.8%) for 90–94%, and 36 (1.8%) for <90%. In soil samples, the numbers were 1354 (56.0%) for ≥ 98%, 821 (33.9%) for 94–98%, 167 (6.9%) for 90–94%, and 77 (3.2%) for <90% (Fig. ). These results indicate that a substantial proportion of cultivable ASVs belong to undescribed taxa. The distribution of these proportions varies across different phyla. Dominant phyla, such as Pseudomonadota , Actinomycetota and Bacillota , have a relatively minor proportion of ASVs (<10%) with best-hit similarity to the database below 94%. However, in many rare phyla, such as Verrucomicrobiota , Acidobacteriota and Gemmatimonadetes in soil, the percentage of ASVs with <94% similarity ranges from 38.6 to 100%. This discrepancy could be attributed to the fact that formally described species within these rare phyla are much less abundant. Metagenomic sequencing for 17 selected agar plate samples produced 517 dereplicated MAGs across 11 phyla (Fig. , Supplementary Data , ). The GTDB-Tk classification revealed that the numbers of MAGs lacking annotation to a formally described taxon at the rank of class, order, family, genus, or species were 1, 3, 6, 65, and 294 (i.e., 56.9% of all MAGs were unannotated at the species level), respectively. This result suggests that metagenomic sequencing of the agar plates is an efficient method for obtaining taxonomically novel genomes. An analysis of the relative abundance of ASVs from cultivation samples revealed that ASVs with high novelty (<94% sequence similarity to the closest type strain) in both soil and AS samples collectively comprised only 0.04% and 0.06% (median value) of the total (Fig. ). This implies that novel taxa are generally present at very low abundance on the agar plates. These low-abundance ASVs remind us of the formation of microcolonies with high taxonomic novelty often observed on plates in previous literature , . Indeed, we identified unneglectable numbers of microcolonies (diameter <300 μm, approximately 20% relative to the number of macrocolonies, see Fig. and Supplementary Fig. ). By measuring the size of these colonies and randomly selecting some (43 microcolonies and 39 macrocolonies) for direct 16S rRNA gene amplification and sequencing, we found that microcolonies were more likely to be taxonomically novel (Fig. ). Similarly, grouping colonies by novelty revealed that those with similarity below 94% and those between 94-98% were significantly smaller than those with similarity above 98% ( P < 0.001, Wilcoxon test, Fig. ). However, the cultivation results indicated that most of these microcolonies failed to subculture on agar plates, obviously more so than the macrocolonies (Fig. ). This observation is consistent with a previous report of low sustainable growth for microcolonies . Sequencing-guided isolation of a strain representing a novel class Among all datasets, in the agar plate samples obtained from an enhanced biological phosphorus removal (EBPR) bioreactor, we identified a highly novel ASV and a MAG without 16S rRNA genes in the amplicon data and the metagenome, respectively (Figs. b and ). The ASV exhibited the closest similarity to Melioribacter roseus (in the phylum Chlorobiota ) with a sequence identity of 87.1%. In contrast, the MAG was classified under the c_ JABWAT01 class of Bacteroidota in the GTDB-Tk database. Notably, the ASV was exclusively detected on agar plates with extraction medium under anaerobic condition (Fig. ), where it reached very high relative abundance (>5%) on a few plates. By picking approximately 60 colonies, we obtained the isolate of the bacterium named as EBPR01. The isolate was found to contain the identical 16S rRNA gene sequence to the ASV and exhibited slow growth as a rod-shaped bacterium (Fig. ), requiring 5 to 7 days for small visible colony formation. Phylogenomic analysis confirmed that this bacterium represents the first isolate of the c_ JABWAT01 (Fig. ). Notably, all four reference genomes affiliated with c_ JABWAT01 originated from laboratory-scale nutrient removal bioreactors, including one also from an EBPR reactor (sp020161835, Fig. ). Therefore, it can be speculated that this lineage possesses specific functions within artificial systems of nutrient removal. General metabolic activity is found to be linked with cultivability As we could define the cultivable and uncultivable bacterial ASVs in inoculum, we designed an experiment to assess whether metabolic activity and short-term adaptivity after exposure to laboratory treatments were related to the cultivability for taxa of an AS sample (see Fig. and Methods ). Here, the metabolic activity of a given ASV was determined by measuring rRNA copies per cell, while adaptivity was quantified as the ratio of metabolic activity after laboratory treatment to that under in-situ conditions (details are provided in Methods ). Metabolic activity was measured under five conditions, including in-situ (O), after exposure to in-situ medium (I), R2A broth (R) for untreated bioflocs (UI and UR), or dispersed cells (DI and DR) in the laboratory for 1 hour. Meanwhile, we conducted cultivation on R2A agar plates for the corresponding ASVs to define their cultivability. Only qualified ASVs (31 cultivable and 48 uncultivable, see Methods ) were assessed for their metabolic activity. The results revealed that the metabolic activity of cultivable ASVs was significantly higher than that of uncultivable ones in all five conditions ( P < 0.001, Wilcoxon test, Fig. ). This suggests that general metabolic activity itself is associated with the laboratory cultivability of environmental bacteria. As shown in Fig. , relative to the in-situ condition (O), UI and UR treatments downregulated metabolic activity in uncultivable ASVs more than in cultivable ASVs ( P < 0.05, Wilcoxon test), while no difference was detected for DI and DR treatments, indicating that short-term adaptivity to laboratory treatments may not be strongly correlated with cultivability. When comparing exposure to in-situ conditions (UI/O and DI/O), both cultivable and uncultivable taxa showed an upregulation of metabolic activity after exposure to R2A broth (UR/O and DR/O), indicating that short-term exposure to artificial media may not impair their metabolic activity. Interestingly, regardless of cultivability, cell dispersion in in-situ medium (DI) led to a marked downregulation of metabolic activity (compared to UI), whereas dispersion in R2A conditions (DR) resulted in an upregulation of metabolic activity (compared to UR). This suggests that dispersion is detrimental to most taxa in in-situ medium (where these microbes form self-assemble aggregates), while it might be conducive to adaptation in artificial nutrient-rich media. 

Among all datasets, in the agar plate samples obtained from an enhanced biological phosphorus removal (EBPR) bioreactor, we identified a highly novel ASV and a MAG without 16S rRNA genes in the amplicon data and the metagenome, respectively (Figs. b and ). The ASV exhibited the closest similarity to Melioribacter roseus (in the phylum Chlorobiota ) with a sequence identity of 87.1%. In contrast, the MAG was classified under the c_ JABWAT01 class of Bacteroidota in the GTDB-Tk database. Notably, the ASV was exclusively detected on agar plates with extraction medium under anaerobic condition (Fig. ), where it reached very high relative abundance (>5%) on a few plates. By picking approximately 60 colonies, we obtained the isolate of the bacterium named as EBPR01. The isolate was found to contain the identical 16S rRNA gene sequence to the ASV and exhibited slow growth as a rod-shaped bacterium (Fig. ), requiring 5 to 7 days for small visible colony formation. Phylogenomic analysis confirmed that this bacterium represents the first isolate of the c_ JABWAT01 (Fig. ). Notably, all four reference genomes affiliated with c_ JABWAT01 originated from laboratory-scale nutrient removal bioreactors, including one also from an EBPR reactor (sp020161835, Fig. ). Therefore, it can be speculated that this lineage possesses specific functions within artificial systems of nutrient removal. 

As we could define the cultivable and uncultivable bacterial ASVs in inoculum, we designed an experiment to assess whether metabolic activity and short-term adaptivity after exposure to laboratory treatments were related to the cultivability for taxa of an AS sample (see Fig. and Methods ). Here, the metabolic activity of a given ASV was determined by measuring rRNA copies per cell, while adaptivity was quantified as the ratio of metabolic activity after laboratory treatment to that under in-situ conditions (details are provided in Methods ). Metabolic activity was measured under five conditions, including in-situ (O), after exposure to in-situ medium (I), R2A broth (R) for untreated bioflocs (UI and UR), or dispersed cells (DI and DR) in the laboratory for 1 hour. Meanwhile, we conducted cultivation on R2A agar plates for the corresponding ASVs to define their cultivability. Only qualified ASVs (31 cultivable and 48 uncultivable, see Methods ) were assessed for their metabolic activity. The results revealed that the metabolic activity of cultivable ASVs was significantly higher than that of uncultivable ones in all five conditions ( P < 0.001, Wilcoxon test, Fig. ). This suggests that general metabolic activity itself is associated with the laboratory cultivability of environmental bacteria. As shown in Fig. , relative to the in-situ condition (O), UI and UR treatments downregulated metabolic activity in uncultivable ASVs more than in cultivable ASVs ( P < 0.05, Wilcoxon test), while no difference was detected for DI and DR treatments, indicating that short-term adaptivity to laboratory treatments may not be strongly correlated with cultivability. When comparing exposure to in-situ conditions (UI/O and DI/O), both cultivable and uncultivable taxa showed an upregulation of metabolic activity after exposure to R2A broth (UR/O and DR/O), indicating that short-term exposure to artificial media may not impair their metabolic activity. Interestingly, regardless of cultivability, cell dispersion in in-situ medium (DI) led to a marked downregulation of metabolic activity (compared to UI), whereas dispersion in R2A conditions (DR) resulted in an upregulation of metabolic activity (compared to UR). This suggests that dispersion is detrimental to most taxa in in-situ medium (where these microbes form self-assemble aggregates), while it might be conducive to adaptation in artificial nutrient-rich media. 

For the MAGs from 6 AS inoculum samples and 12 corresponding agar plate samples (detailed in Supplementary Data ), we defined MAGs with cultivable and uncultivable phenotypes (see Methods ). This discrimination allowed us to perform comparative genomics to screen for genotypes associated with the phenotypes. To avoid potential false signals that might be enriched in different phylogenetic branches with uneven numbers of MAGs, it was crucial to conduct this analysis within a lineage where the two groups were phylogenetically intermixed. In our study, we focused on the order Burkholderiales , which contains many dominant and core taxa in global wastewater treatment systems , and comprises 111 cultivable and 29 uncultivable MAGs in our dataset (Supplementary Data ). Phylogenomic analysis showed that the genomes of the two groups were moderately mixed within this order (Supplementary Fig. ). Comparative genomic analysis unveiled a significant enrichment of certain clusters of orthologous genes (COGs) within the cultivable ( n = 66) and uncultivable ( n = 10) groups, respectively (Fig. and Supplementary Data ). While many of these genes did not exhibit clear functional associations with cultivability, we identified two potential features correlated with cultivability that were underrepresented in the uncultivable group. The first catalog includes six COGs associated with vitamin B 12 biosynthesis. Vitamin B 12 is considered a widely shared growth factor in microbial communities due to its complex biosynthetic pathway and extremely low demand, aligning with the ‘Black Queen hypothesis' . Notably, all but one examined Burkholderiales genomes in our study could be classified as vitamin B 12 -dependent, as per the definition by Shelton et al. . (Supplementary Fig. ). The second feature includes two COGs linked to oxidative stress tolerance (COG3022 and COG2717). To validate whether adding vitamin B 12 to R2A medium can enhance the cultivability of Burkholderiales , we compared cultivable bacteria in two additional AS samples (XM4 and ZZ1) under two conditions: with and without the addition of vitamin B 12 (5 μg/L). As depicted in Fig. , both samples from wastewater treatment plants displayed an increased number of detectable cultivable Burkholderiales ASVs ( P < 0.05, one-tailed t -test). In the case of the XM4 sample, the number of total cultivable ASVs also increased significantly ( P < 0.001, one-tailed t -test). Comparing the relative abundance of low-abundance Burkholderiales ASVs (<0.03% on average) detected on the control plates and their relative abundance in the treatment, both samples showed an increase of relative abundance of these low-abundance ASVs (potential slow growers) with the addition of vitamin B 12 ( P < 0.01, paired one-tailed t -test, Fig. ). These results suggest that the addition of vitamin B 12 enhanced the growth of these bacteria on the plates, thereby increasing the likelihood of their detection through amplicon sequencing. Therefore, these results validate that the differences in cultivable and non-cultivable genotypes obtained from comparative genomics can guide improvements in culture conditions. 

The definition of cultivable and uncultivable phenotypes under specific cultivation conditions provided the foundation for assessing PCT and advancing cultivation strategies based on taxonomic and genomic information obtained through sequencing. In our terminology, we used the term “uncultivable” rather than “uncultured”, as cultivability was determined by actual experimental attempts. As commented on by Steen et al. . and supported by our results, the method for determining cultivable phenotypes for taxa based on the approach applied by Martiny had two primary limitations: (1) It could not ascertain whether bacteria with sequences cloned from the environment could be successfully cultivated under specific conditions (i.e., incorrectly referred to as uncultured taxa); (2) Phylogenetically related taxa did not consistently exhibit the same cultivable phenotype under identical or similar conditions. This was in line with the limited phylogenetic conservatism observed in carbon source utilization , . Similarly, it was not feasible to categorize MAGs and single-cell genomes obtained from environmental samples as uncultivable bacteria without actual cultivation attempts. The overarching conclusion that PCT surpassed PCC implied a potential underestimation of environmental bacterial cultivability in previous assessments. PCC was apparently influenced by the innate characteristics of the inoculated microbial community. Clearly, when a sample was dominated by taxa belonging to uncultivable groups, its PCC tended to be relatively low . Other factors, such as the presence of viable but non-cultivable cells, could also have contributed to the disparity between PCT and PCC , as could non-viable or dormant cells. To refute or accept the statement that “most environmental bacteria are uncultivable,” further validation was required, as we observed a slight downward trend in overall cultivability with an increase in sample size. However, for PCT values of individual samples, we hypothesized that they might still be underestimated for two main reasons. First, we observed a trend of increasing PCT values with higher sequencing depths. This trend might have signified the presence of microcolonies, with their detection becoming more likely as sequencing depth increased in high-throughput sequencing. Second, our experiments covered only a limited number of cultivation conditions. A more systematic culturomic design could have generated higher PCTs. Moreover, a significant number of ASVs cultured on agar plates were not detected in the inoculum. While we could not definitively ascertain how this phenomenon impacted the PCT values, it implied the potential cultivation of numerous rare taxa, a finding corroborated by previous reports . It should be noted that cultivability did not always equate to isolatability. Our results indicated that a substantial portion of bacteria in microcolonies ceased to grow during subculture, a phenomenon documented in earlier literature , , . Some bacteria might have required co-cultivation helpers for successful isolation , . Recent research has also pointed to the existence of inter-colony co-occurrence and potential cross-feeding effects on agar plates . The subsequent failure of isolation cultivation might, in part, have been attributed to the disruption of such relationships. Meanwhile, the impact of these neighboring relationships on our PCT assessment remained unclear. This study is not the first to profile the taxonomic composition of cultivable microbiota growing on agar plates using high-throughput sequencing and guide isolation – . Perhaps owing to the use of multiple cultivation conditions and environmental samples, we isolated a bacterium representing a novel class, validated that taxonomic profiling for cultivable taxa could be used to recover more representatives of high taxonomic ranks, even fill the gaps of microbial dark matter. To achieve this, one challenge that needed to be addressed was the slow growth rate many undescribed or hitherto uncultured taxa. Our findings related to this issue included: (1) the low relative abundance of many novel cultivable taxa in amplicon datasets; (2) the presence of microcolonies with poor success rates in sub-cultivation; (3) the implications of relatively low metabolic activity (potentially related to growth rate) and uncultivable phenotype. Slowly growing microorganisms on agar plates have been addressed widely. In specific cases, altering the gelling agent or culturing using single-cell droplets could enhance their growth , . Studies have demonstrated that low-nutrient conditions (optimal for low metabolic activity bacteria) and the dilution-to-extinction method (which eliminates competition) can significantly enhance the proportion of cultivable bacteria by addressing these issues , . Additionally, adding resuscitation factors , signaling molecules , iron carriers , etc., might also improve cultivability by activating cell metabolism. The combination of these approaches with the cultivable phenotype determination proposed in this study holds the potential to further refine estimations and guidance for cultivability. Finally, our study introduced a strategy in which phenotype-genotype-guided cultivation was employed to enhance the cultivability of a certain bacterial lineage. Experimental results demonstrated that the addition of vitamin B 12 effectively stimulated the growth of targeted taxa. Furthermore, additional COGs associated with oxidative stress adaptation, a common challenge in laboratory cultivation of microorganisms , , , were identified. Although these factors were not new to bacterial cultivation, they underscored the effectiveness of this strategy. Genotype profiling also revealed numerous differentially enriched genes with both known and unknown functions that could not be directly linked to specific cultivation conditions. On one hand, exploring the functions of these genes and pathways (especially for those related to growth in laboratory conditions) was a valuable pursuit. On the other hand, like optimization strategies in genome-wide association studies, expanding the dataset with greater phylogenetic diversity could significantly reduce noise and false signals. These enhancements ensured greater reliability in identifying optimal conditions and potentially elucidating the underlying key factors for the uncultivable taxa at higher taxonomic levels. Furthermore, combining the cultivation phenotype with insights from transcriptomics , proteomics , and metabolomics might be necessary to address these challenges more comprehensively. 

Environmental samples As depicted in Fig. , this study utilized two representative environmental samples, specifically, soil ( n = 6) and activated sludge (AS) collected from full-scale wastewater treatment plants ( n = 12, five used in PCT calculation) and a laboratory EBPR bioreactor ( n = 1). The reason for selecting these two types of samples was that they possessed high bacterial diversity and were easy to collect. Detailed information pertaining to these samples is provided in Supplementary Data . Soil samples were collected and transported to the laboratory with the elapsed time ranging from 1 h to 3 days, followed by sieving. For AS samples, the times ranged from 0.5 h to 1 day. Given the necessity to prepare extraction media for these samples, a portion of the samples collected on the day of arrival was used for extraction media preparation. Inoculation onto agar plates was conducted approximately one day after the preparation of the agar plates. The AS samples underwent cell dispersion using glass homogenizers, while soil samples were subjected to vortexing (at maximum intensity for 5 min) to facilitate cell dispersion. For the inoculum, single-cell filtrates were obtained by passing the dispersed suspensions through a 5 µm pore-size filter membrane (Isopore, Merck, Germany). Following the collection of these filtrates for inoculation, fluorescence staining and microscopic cell counting were promptly carried out to determine cell density in the inocula . Subsequently, appropriate dilutions were made for plating based on the cell counting results. Microbial cells from approximately 50–100 mL of filtrates were harvested using a 0.1 µm pore-size membrane (mixed cellulose esters, Merck, Germany) as the bacterial community of the inocula. Both the original samples and the filtrate inoculum from the soil and AS samples were stored at -20 °C for subsequent DNA extraction. Media and culturing conditions For the common laboratory media, microbial cells from soil and AS were cultured on 1% strength Tryptic Soy Agar (TSA) and full-strength R2A agar media, respectively. These media were widely used in culturing environmental bacteria. We also prepared extraction media from each sample. For soil samples, 500 g of soil was introduced into 2 liters of an inorganic salt solution (K 2 HPO 4 0.02 g L ˗1 , MgSO4·7H 2 O 0.02 g L -1 , KNO 3 0.02 g L -1 , pH adjusted to 7.0) and then stirred using a magnetic stirrer for 2 hours. For AS samples, sewage containing AS was allowed to settle for 1 hour before collecting the supernatant. In the case of the sample from the EBPR bioreactor, an additional medium was prepared as the artificial influent . These suspensions underwent sequential steps of clarification, involving filtration through glass filter paper (average pore-size 2.7 µm glass filter papers, GF/D, Whatman, UK), 0.45 µm pore-size filter membranes, and 0.1 µm pore-size filter membranes (mixed cellulose esters, Merck, Germany). Subsequently, sterile filtrates were manually prepared using syringe filters (0.22 µm pore-size, Millex-GV, Merck, Germany). Considering a prior study that indicated the production of reactive oxygen species (ROS) inhibiting microbial growth when sterilizing agar mixed with phosphate-containing media , the synthetic and extraction media were sterilized separately from the agar. Subsequently, autoclaved 6% agar (in ddH 2 O) was mixed in a 4:1 ratio with prewarmed (50 °C) sterilized extraction or artificial media before pouring (with the final concentration of agar being 1.2%). For plating, three or four dilution gradients (diluted with sterilized 0.85% NaCl solution) were established with ten replications per gradient. Typically, 5000 to 500,000 cells were plated on each agar plate. The incubation was conducted at 26 °C for 4 weeks. Throughout the incubation, various oxygen availabilities were applied: aerobic (AE, ambient atmosphere), anaerobic (AN, 80% nitrogen, 15% carbon dioxide, and 5% hydrogen in an anaerobic glovebox), and microaerophilic (MI, like anaerobic but with 0.1–0.3% oxygen, monitored and adjusted within a glovebox). Not all media and availability of oxygen were tested for all samples. Detailed cultivation conditions for each sample are presented in Supplementary Data . DNA extraction The original soil and AS samples underwent DNA extraction using a kit, following the manufacturer’s instructions (FastDNA® SPIN Kit for Soil, MP Biomedicals, US). For the inoculum samples collected on filter membranes, the membranes were aseptically cut into pieces and then extracted using the same kit mentioned above. After CFU counting, for each agar plate containing typically 100 to 800 colonies from the same dilution level, sterile cell scrapers were used to collect the grown cells. Three milliliters of sterile 0.85% NaCl solution were added, and all visible colonies were scraped and transferred into two 1.5 mL centrifuge tubes. After centrifugation at 10,000 × g for 5 min, the supernatant was removed, and DNA extraction from the combined pellet was performed using the same kit. The extracted DNA was subsequently assessed for concentration and purity using a microspectrophotometer. In most cases, six agar plates for each culture condition and ≥3 technical (PCR-level) replications of both the original samples and the filter membrane samples were analyzed for amplicon sequencing (detailed in Supplementary Data ). High throughput sequencing and amplicon data analysis The extracted DNA underwent barcode-PCR amplification targeting the V4 region of the bacterial 16S rRNA gene, following the protocols detailed in the literature , . V4 amplicons were generated using the 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) primers. To minimize cross-talk between samples, unique dual barcodes were used for each sample . After purification, the mixed PCR products were sent to a commercial service provider (Novogene, China) for library preparation ( n = 16) and sequencing using the PE250 strategy (Novaseq 6000, Illumina, US). For the clean data received from the service provider, denoising and ASV (species-level threshold for the V4 region proposed by Edgar ) picking were carried out using the USEARCH and VSEARCH platforms. Taxonomic annotation for ASVs was conducted on the Mothur platform , referring to the RDP taxonomic training set version No. 19 (method=wang, threshold=80%) . When determining taxonomic novelty of cultivable ASVs, only sequences of type strains from the RDP database (nearly 19,000 sequences after removing those from non-type strains) were used, employing BLASTN searching. The ASVs were cataloged into four levels of novelty, i.e., with the closest type strain sharing ≥98%, 94–98%, 90–94%, and <90% sequence similarities (requiring >95% coverage). Unless otherwise specified, 10,000 reads were subsampled per sample for subsequent analyses, with various sequencing depths employed for specific analyses. In total, 515 amplicon datasets (394 agar plate samples) were obtained in this study (Supplementary Data ). Removal of contaminant ASVs in the amplicon datasets Contaminants widely occurred in high-throughput sequencing techniques, although low biomass samples were more likely impacted. Undoubtedly, contaminant ASVs could introduce bias into our PCT calculation. Therefore, we employed the Decontam strategy to assess and identify contamination in the amplicon sequencing datasets within our experimental framework . To evaluate potential contamination, we utilized a unique bacterial strain NS6-2 (the closest type strain was Luteibaculum oceani CC-AMWY-103B with only 88.9% similarity of 16S rRNA gene). This strain was isolated from Antarctic seawater, and its closely related lineage was not expected to be present in our samples, so it was chosen as the internal standard. Prior to PCR, we introduced the NS6-2 genome into the DNA extraction blank with three-fold dilutions (3 × 10 3 , 9 × 10 3 , 2.7 × 10 4 , 8.1 × 10 4 , 2.43 × 10 5 , 7.29 × 10 5 copies per reaction) in triplicate. These internal standards were independently sequenced in two separate libraries, each containing amplicons of one AS sample. In the internal standards of each library, non-NS6-2 ASVs were screened based on a Pearson correlation coefficient >0.8 between their relative abundance and the dilution rates. Due to the potential for low-frequency cross-talk of the true ASVs from our samples to these internal standard samples , especially for those with low copy number, we refrained from directly removing all these ASVs in all samples. For each candidate contaminant ASV (listed in Supplementary Data ), if its relative abundance in the 3 × 10 3 internal standard sample (the average value of triplicates for each library, referring to the higher value in the two libraries) exceeded the maximum relative abundance observed in any sample of one library, it was considered as contamination only for the corresponding library. The contamination ASVs were then removed from the downstream analysis for the corresponding library. Determination of PCC and PCT The determination of PCC followed established protocols as described in prior studies . Briefly, we quantified the bacterial cell density of the inoculum via fluorescence staining and microscopic examination. The number of colony-forming units (CFUs) was manually counted after a 4-week incubation period. PCC was calculated by dividing the number of CFUs by the number of inoculated cells spread on each plate. The same plates used for calculating PCC were also used for PCT determination, allowing paired comparisons on PCC and PCT values. To calculate PCT, the number of ASVs detected in each agar plate sample, which were also detected in the inoculum, was divided by the total number of ASVs detected in the inoculum. Once the ASV table was generated, PCT values could be calculated for each plate (plate-level), all plates for a specific condition (condition-level), and all plates of all culture conditions for a given sample (sample-level). Examinations on microcolonies Microcolonies were defined as colonies with a diameter smaller than 300 µm, while those larger than 300 µm were termed macrocolonies. The sizes of colonies were measured under a stereomicroscope. In one experiment using R2A media, microcolonies ( n = 43) and macrocolonies ( n = 39) were picked for direct amplification of the full-length 16S rRNA gene using primers 27 F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492 R (5′-GGTTACCTTGTTACGACTT-3′) via PCR. The taxonomic novelty of these colonies was examined by referring to the RDP type strain database, as mentioned above. Due to the inability to perform PCR and subculture simultaneously on specific microcolonies, additional microcolonies ( n = 26) and macrocolonies ( n = 22) were randomly selected for subculture examination. These colonies were inoculated into corresponding liquid media (i.e., without agar) and were incubated for 14 days. Turbidity was measured, and cases where the optical density at 600 nm wavelength exceeded 0.05 were considered successful for subculture. Isolation and analysis on the strain EBPR01 In the amplicon data obtained from the cultivation samples of the EBPR reactor, we identified an ASV exhibiting high taxonomic novelty that was exclusively present under a specific culture condition (extraction medium and anaerobic conditions) with a high relative abundance. Subsequently, we selected approximately 60 colonies from the remaining agar plates that had not been scrapped. We performed streak purification under the same culture condition for these colonies. Guided by sequencing of the 16S rRNA gene, we isolated a strain named EBPR01 with the identical V4 sequence. We observed the morphology of the strain under a transmission electron microscope and obtained its draft genome through next-generation sequencing. The GTDB-Tk system classified it within an uncultured class of Bacteroidota , prompting us to select representatives of all orders in this phylum along with outgroup sequences for phylogenetic reconstruction based on the bacterial 120 universal genes using FastTree (v2.1.10) software . Measurement of metabolic activity for cultivable and uncultivable taxa To explore potential differences in the metabolic activity of taxa with cultivable and uncultivable phenotypes under both in-situ conditions (i.e., in intact bioflocs and the AS suspension) and laboratory conditions (dispersal and exposure to R2A broth, simulating the isolation process), we utilized rRNA copies per cell as a metric for measuring metabolic activity for a given ASV. We conducted the following experiments. An AS sample (XM3) was collected and transported to the laboratory within 0.5 h. One portion of the untreated sample (referred to as “O”) represented the in-situ status of the microbiome and was fixed on-site with RNALater in triplicate. On one hand, we conducted R2A agar plate cultivation and sequencing to distinguish cultivable and uncultivable ASVs as described earlier. On the other hand, a fraction of the AS was subjected to dispersion treatment (homogenized by glass grinders) and sieved through a 2000-mesh sieve (no filtration through membranes, to reduce operational time and minimize non-targeted stimuli to the greatest extent, with pore size of approximately 6 to 7 μm, retaining most cells in a single-cell form) for approximately 30 min. Subsequently, the untreated (U) and dispersed (D) AS samples were exposed to R2A broth (UR and DR) or 0.22 µm pore-size membrane-filtered in-situ medium (UI and DI) in triplicate, with each suspension adjusted to ensure similar biomass levels between the U and D treatments (guided by a pre-experiment, OD600 ≈ 0.2). A 2-mL suspension was collected after 1 hour of treatment and fixed in RNALater. All fixed samples were stored at -80 °C before simultaneous extraction of RNA and DNA according to an established protocol for AS samples . After extraction, RNA was immediately reverse-transcribed into cDNA (PrimeScript RT reagent Kit with gDNA Eraser, Takara Bio, China). These DNA and cDNA samples were PCR-amplified and sequenced for the V4 region of the 16S rRNA gene as described above (see Supplementary Data for sample information). Absolute quantitative PCR (qPCR) was conducted to quantify the copy numbers of ribosomal RNA gene (rDNA) and ribosomal RNA (rRNA, in cDNA form) in the samples. Assuming consistent extraction efficiency across different samples and taxa (at least no systematic bias between cultivable and uncultivable taxa within the same suspensions), and maintaining consistency in the experimental system and sampling volumes, the copy numbers of rDNA and rRNA in each extraction could represent the copy number density (i.e., the number of copies per milliliter) for each ASV. A standard curve (R 2 = 0.993) ranging from 10 4 to 10 8 copies per reaction was generated for absolute quantification using a cloned 16S rRNA gene from Escherichia coli as the standard template (in pMD19-T vector). This quantification was performed in triplicate technical replicates. Simultaneously, the amplicon sequencing data allowed us to calculate the proportions of each ASV relative to all bacteria. By multiplying these proportions by the total copy numbers, we obtained the copy number density of 16S rRNA (cDNA) and rDNA for each dominant ASV in each treatment. We excluded ASVs with insufficient abundance that could potentially cause bias (retaining those ≥0.1% in all rDNA samples, which were normalized to 20,000 reads). To determine the rRNA copies per cell for these dominant ASVs, the rDNA copy number for each ASV was normalized by the number of genomic ribosomal RNA operons ( rrn ) obtained from the rrn DB . The resulting rRNA copies per cell for both cultivable and uncultivable ASVs were used as indicators of their metabolic activity. We compared these values between cultivable and uncultivable ASVs, as well as their dynamics under four treatments (UI, DI, UR, and DR) relative to the original condition (O), to assess their short-term adaptability. Metagenomic analysis To decipher the potential metabolic features of bacterial taxa defined as cultivable and uncultivable under examined cultivation conditions, and then to infer strategies for improving cultivation, their genomes were obtained through genome-centered metagenomic sequencing. We performed metagenomic sequencing on 6 inoculum samples and 17 agar plate samples derived from AS (detailed in Supplementary Data ) using the HiseqX10 sequencing platform (Illumina, US) with a PE150 sequencing strategy (commercial service by Novogene, China). These 17 samples were selected because their 16S rRNA gene profiles exhibited high alpha and beta diversity, and they contained novel taxa with higher relative abundance than others, which allowed for a greater likelihood of obtaining MAGs with taxonomic diversity and novelty. Following quality-control filtering, the metagenomic reads were assembled using MEGAHIT v1.2.9 . Genome binning and primary dereplication were accomplished by utilizing metaWRAP v1.2.2 and dRep v3.3.1 , respectively. The genomic quality was evaluated using CheckM v1.0.12, leading to the exclusion of genomes with less than 50% completeness or over 10% contamination . To reduce redundancy of metagenome-assembled genomes (MAGs) from different cultivation conditions, those exhibiting an average nucleotide identity (ANI) of over 99% from the same sample source were further dereplicated. The one with highest quality (completeness-5×contamination) were retained. Taxonomic annotation of the MAGs was conducted utilizing GTDB-Tk v2.3.0 with GTDB-r214 . Open reading frames were predicted using Prodigal v2.6.3 , and functional annotation as clusters of orthologous groups (COGs) was carried out using the eggNOG-mapper v2 . Promoting cultivability for a specific taxon via comparative genomics To categorize MAGs into cultivable or uncultivable phenotypes (Supplementary Data ), the following steps were carried out. MAGs obtained from 12 agar plate samples (with corresponding inoculum samples) were designated as cultivable phenotypes. However, MAGs derived from 6 inoculum samples could not be directly defined as uncultivable ones due to two reasons. First, highly similar MAGs (ANI > 99%) could be obtained from both cultivation samples and inoculum samples. Second, MAGs represented only about 40% of the reads (ranging from 14 to 70%) from each agar plate metagenome. The remaining reads, which are also from cultivable taxa, might have matched MAGs that were obtained in the inoculum metagenomes. Therefore, uncultivable MAGs were defined as follows: If selected marker genes (DNA gyrase subunit B and RNA polymerase subunit B) of a MAG extracted from inoculum samples were absent or only detected in the agar plate metagenomes with <1× coverage per 10 GB (based on read mapping and the average of the two genes), these MAGs were defined as uncultivable. To mitigate potential false signals arising from the separated phylogeny of cultivated and uncultivated MAGs, we focused our further investigation on the largest order in our genome catalog, Burkholderiales , which comprised 111 cultivable and 29 uncultivable genomes. This order was widely and dominantly distributed in global wastewater treatment plants , . We conducted a comparative statistical analysis of the presence/absence frequency of COGs (requiring presence in >5% MAGs) for cultivable and uncultivable MAGs within the order. We examined the COGs that were enriched in either group ( P < 0.05, Fisher’s exact test). Based on the functional annotation of these COGs, our objective was to deduce potential conditions related to uncultivability. Of particular interest was the significant underrepresentation of six vitamin B 12 synthesis genes in the uncultivable group compared with the cultivable group. This observation prompted us to conduct cultivation experiments to validate whether the addition of vitamin B 12 could promote the cultivable diversity of this order. For the validation experiments, we used R2A medium and compared the outcomes with or without the addition of 5 μg/L of vitamin B 12 . It is worth noting that R2A medium contained yeast extract, which may have contained approximately 0.5 μg/L of vitamin B 12 , according to Tao et al. . Meanwhile, it was reported that different microorganisms within a community exhibited selectivity for different subtypes of vitamin B 12 , but high concentrations of vitamin B 12 could reduce or even eliminate subtype specificity . The cultivation experiments were conducted on two AS samples (XM4 and ZZ; see Supplementary Data ). Colonies from the agar plates were collected for sequencing and analysis. Due to differences between the taxonomic annotation systems of 16S rRNA genes and the GTDB-Tk system (where Rhodocyclaceae is a family within Burkholderiales ), we assessed the richness and relative abundance of ASVs affiliated with both Burkholderiales and Rhodocyclales (referring to the RDP database) in different treatments. Both orders exhibited high diversity in the original and culturomics samples. Data visualization The R package dplyr was used for data manipulation . Plots were generated using R packages ggplot2 and pheatmap , . Reporting summary Further information on research design is available in the linked to this article. 

As depicted in Fig. , this study utilized two representative environmental samples, specifically, soil ( n = 6) and activated sludge (AS) collected from full-scale wastewater treatment plants ( n = 12, five used in PCT calculation) and a laboratory EBPR bioreactor ( n = 1). The reason for selecting these two types of samples was that they possessed high bacterial diversity and were easy to collect. Detailed information pertaining to these samples is provided in Supplementary Data . Soil samples were collected and transported to the laboratory with the elapsed time ranging from 1 h to 3 days, followed by sieving. For AS samples, the times ranged from 0.5 h to 1 day. Given the necessity to prepare extraction media for these samples, a portion of the samples collected on the day of arrival was used for extraction media preparation. Inoculation onto agar plates was conducted approximately one day after the preparation of the agar plates. The AS samples underwent cell dispersion using glass homogenizers, while soil samples were subjected to vortexing (at maximum intensity for 5 min) to facilitate cell dispersion. For the inoculum, single-cell filtrates were obtained by passing the dispersed suspensions through a 5 µm pore-size filter membrane (Isopore, Merck, Germany). Following the collection of these filtrates for inoculation, fluorescence staining and microscopic cell counting were promptly carried out to determine cell density in the inocula . Subsequently, appropriate dilutions were made for plating based on the cell counting results. Microbial cells from approximately 50–100 mL of filtrates were harvested using a 0.1 µm pore-size membrane (mixed cellulose esters, Merck, Germany) as the bacterial community of the inocula. Both the original samples and the filtrate inoculum from the soil and AS samples were stored at -20 °C for subsequent DNA extraction. 

For the common laboratory media, microbial cells from soil and AS were cultured on 1% strength Tryptic Soy Agar (TSA) and full-strength R2A agar media, respectively. These media were widely used in culturing environmental bacteria. We also prepared extraction media from each sample. For soil samples, 500 g of soil was introduced into 2 liters of an inorganic salt solution (K 2 HPO 4 0.02 g L ˗1 , MgSO4·7H 2 O 0.02 g L -1 , KNO 3 0.02 g L -1 , pH adjusted to 7.0) and then stirred using a magnetic stirrer for 2 hours. For AS samples, sewage containing AS was allowed to settle for 1 hour before collecting the supernatant. In the case of the sample from the EBPR bioreactor, an additional medium was prepared as the artificial influent . These suspensions underwent sequential steps of clarification, involving filtration through glass filter paper (average pore-size 2.7 µm glass filter papers, GF/D, Whatman, UK), 0.45 µm pore-size filter membranes, and 0.1 µm pore-size filter membranes (mixed cellulose esters, Merck, Germany). Subsequently, sterile filtrates were manually prepared using syringe filters (0.22 µm pore-size, Millex-GV, Merck, Germany). Considering a prior study that indicated the production of reactive oxygen species (ROS) inhibiting microbial growth when sterilizing agar mixed with phosphate-containing media , the synthetic and extraction media were sterilized separately from the agar. Subsequently, autoclaved 6% agar (in ddH 2 O) was mixed in a 4:1 ratio with prewarmed (50 °C) sterilized extraction or artificial media before pouring (with the final concentration of agar being 1.2%). For plating, three or four dilution gradients (diluted with sterilized 0.85% NaCl solution) were established with ten replications per gradient. Typically, 5000 to 500,000 cells were plated on each agar plate. The incubation was conducted at 26 °C for 4 weeks. Throughout the incubation, various oxygen availabilities were applied: aerobic (AE, ambient atmosphere), anaerobic (AN, 80% nitrogen, 15% carbon dioxide, and 5% hydrogen in an anaerobic glovebox), and microaerophilic (MI, like anaerobic but with 0.1–0.3% oxygen, monitored and adjusted within a glovebox). Not all media and availability of oxygen were tested for all samples. Detailed cultivation conditions for each sample are presented in Supplementary Data . 

The original soil and AS samples underwent DNA extraction using a kit, following the manufacturer’s instructions (FastDNA® SPIN Kit for Soil, MP Biomedicals, US). For the inoculum samples collected on filter membranes, the membranes were aseptically cut into pieces and then extracted using the same kit mentioned above. After CFU counting, for each agar plate containing typically 100 to 800 colonies from the same dilution level, sterile cell scrapers were used to collect the grown cells. Three milliliters of sterile 0.85% NaCl solution were added, and all visible colonies were scraped and transferred into two 1.5 mL centrifuge tubes. After centrifugation at 10,000 × g for 5 min, the supernatant was removed, and DNA extraction from the combined pellet was performed using the same kit. The extracted DNA was subsequently assessed for concentration and purity using a microspectrophotometer. In most cases, six agar plates for each culture condition and ≥3 technical (PCR-level) replications of both the original samples and the filter membrane samples were analyzed for amplicon sequencing (detailed in Supplementary Data ). 

The extracted DNA underwent barcode-PCR amplification targeting the V4 region of the bacterial 16S rRNA gene, following the protocols detailed in the literature , . V4 amplicons were generated using the 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) primers. To minimize cross-talk between samples, unique dual barcodes were used for each sample . After purification, the mixed PCR products were sent to a commercial service provider (Novogene, China) for library preparation ( n = 16) and sequencing using the PE250 strategy (Novaseq 6000, Illumina, US). For the clean data received from the service provider, denoising and ASV (species-level threshold for the V4 region proposed by Edgar ) picking were carried out using the USEARCH and VSEARCH platforms. Taxonomic annotation for ASVs was conducted on the Mothur platform , referring to the RDP taxonomic training set version No. 19 (method=wang, threshold=80%) . When determining taxonomic novelty of cultivable ASVs, only sequences of type strains from the RDP database (nearly 19,000 sequences after removing those from non-type strains) were used, employing BLASTN searching. The ASVs were cataloged into four levels of novelty, i.e., with the closest type strain sharing ≥98%, 94–98%, 90–94%, and <90% sequence similarities (requiring >95% coverage). Unless otherwise specified, 10,000 reads were subsampled per sample for subsequent analyses, with various sequencing depths employed for specific analyses. In total, 515 amplicon datasets (394 agar plate samples) were obtained in this study (Supplementary Data ). 

Contaminants widely occurred in high-throughput sequencing techniques, although low biomass samples were more likely impacted. Undoubtedly, contaminant ASVs could introduce bias into our PCT calculation. Therefore, we employed the Decontam strategy to assess and identify contamination in the amplicon sequencing datasets within our experimental framework . To evaluate potential contamination, we utilized a unique bacterial strain NS6-2 (the closest type strain was Luteibaculum oceani CC-AMWY-103B with only 88.9% similarity of 16S rRNA gene). This strain was isolated from Antarctic seawater, and its closely related lineage was not expected to be present in our samples, so it was chosen as the internal standard. Prior to PCR, we introduced the NS6-2 genome into the DNA extraction blank with three-fold dilutions (3 × 10 3 , 9 × 10 3 , 2.7 × 10 4 , 8.1 × 10 4 , 2.43 × 10 5 , 7.29 × 10 5 copies per reaction) in triplicate. These internal standards were independently sequenced in two separate libraries, each containing amplicons of one AS sample. In the internal standards of each library, non-NS6-2 ASVs were screened based on a Pearson correlation coefficient >0.8 between their relative abundance and the dilution rates. Due to the potential for low-frequency cross-talk of the true ASVs from our samples to these internal standard samples , especially for those with low copy number, we refrained from directly removing all these ASVs in all samples. For each candidate contaminant ASV (listed in Supplementary Data ), if its relative abundance in the 3 × 10 3 internal standard sample (the average value of triplicates for each library, referring to the higher value in the two libraries) exceeded the maximum relative abundance observed in any sample of one library, it was considered as contamination only for the corresponding library. The contamination ASVs were then removed from the downstream analysis for the corresponding library. 

The determination of PCC followed established protocols as described in prior studies . Briefly, we quantified the bacterial cell density of the inoculum via fluorescence staining and microscopic examination. The number of colony-forming units (CFUs) was manually counted after a 4-week incubation period. PCC was calculated by dividing the number of CFUs by the number of inoculated cells spread on each plate. The same plates used for calculating PCC were also used for PCT determination, allowing paired comparisons on PCC and PCT values. To calculate PCT, the number of ASVs detected in each agar plate sample, which were also detected in the inoculum, was divided by the total number of ASVs detected in the inoculum. Once the ASV table was generated, PCT values could be calculated for each plate (plate-level), all plates for a specific condition (condition-level), and all plates of all culture conditions for a given sample (sample-level). 

Microcolonies were defined as colonies with a diameter smaller than 300 µm, while those larger than 300 µm were termed macrocolonies. The sizes of colonies were measured under a stereomicroscope. In one experiment using R2A media, microcolonies ( n = 43) and macrocolonies ( n = 39) were picked for direct amplification of the full-length 16S rRNA gene using primers 27 F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492 R (5′-GGTTACCTTGTTACGACTT-3′) via PCR. The taxonomic novelty of these colonies was examined by referring to the RDP type strain database, as mentioned above. Due to the inability to perform PCR and subculture simultaneously on specific microcolonies, additional microcolonies ( n = 26) and macrocolonies ( n = 22) were randomly selected for subculture examination. These colonies were inoculated into corresponding liquid media (i.e., without agar) and were incubated for 14 days. Turbidity was measured, and cases where the optical density at 600 nm wavelength exceeded 0.05 were considered successful for subculture. 

In the amplicon data obtained from the cultivation samples of the EBPR reactor, we identified an ASV exhibiting high taxonomic novelty that was exclusively present under a specific culture condition (extraction medium and anaerobic conditions) with a high relative abundance. Subsequently, we selected approximately 60 colonies from the remaining agar plates that had not been scrapped. We performed streak purification under the same culture condition for these colonies. Guided by sequencing of the 16S rRNA gene, we isolated a strain named EBPR01 with the identical V4 sequence. We observed the morphology of the strain under a transmission electron microscope and obtained its draft genome through next-generation sequencing. The GTDB-Tk system classified it within an uncultured class of Bacteroidota , prompting us to select representatives of all orders in this phylum along with outgroup sequences for phylogenetic reconstruction based on the bacterial 120 universal genes using FastTree (v2.1.10) software . 

To explore potential differences in the metabolic activity of taxa with cultivable and uncultivable phenotypes under both in-situ conditions (i.e., in intact bioflocs and the AS suspension) and laboratory conditions (dispersal and exposure to R2A broth, simulating the isolation process), we utilized rRNA copies per cell as a metric for measuring metabolic activity for a given ASV. We conducted the following experiments. An AS sample (XM3) was collected and transported to the laboratory within 0.5 h. One portion of the untreated sample (referred to as “O”) represented the in-situ status of the microbiome and was fixed on-site with RNALater in triplicate. On one hand, we conducted R2A agar plate cultivation and sequencing to distinguish cultivable and uncultivable ASVs as described earlier. On the other hand, a fraction of the AS was subjected to dispersion treatment (homogenized by glass grinders) and sieved through a 2000-mesh sieve (no filtration through membranes, to reduce operational time and minimize non-targeted stimuli to the greatest extent, with pore size of approximately 6 to 7 μm, retaining most cells in a single-cell form) for approximately 30 min. Subsequently, the untreated (U) and dispersed (D) AS samples were exposed to R2A broth (UR and DR) or 0.22 µm pore-size membrane-filtered in-situ medium (UI and DI) in triplicate, with each suspension adjusted to ensure similar biomass levels between the U and D treatments (guided by a pre-experiment, OD600 ≈ 0.2). A 2-mL suspension was collected after 1 hour of treatment and fixed in RNALater. All fixed samples were stored at -80 °C before simultaneous extraction of RNA and DNA according to an established protocol for AS samples . After extraction, RNA was immediately reverse-transcribed into cDNA (PrimeScript RT reagent Kit with gDNA Eraser, Takara Bio, China). These DNA and cDNA samples were PCR-amplified and sequenced for the V4 region of the 16S rRNA gene as described above (see Supplementary Data for sample information). Absolute quantitative PCR (qPCR) was conducted to quantify the copy numbers of ribosomal RNA gene (rDNA) and ribosomal RNA (rRNA, in cDNA form) in the samples. Assuming consistent extraction efficiency across different samples and taxa (at least no systematic bias between cultivable and uncultivable taxa within the same suspensions), and maintaining consistency in the experimental system and sampling volumes, the copy numbers of rDNA and rRNA in each extraction could represent the copy number density (i.e., the number of copies per milliliter) for each ASV. A standard curve (R 2 = 0.993) ranging from 10 4 to 10 8 copies per reaction was generated for absolute quantification using a cloned 16S rRNA gene from Escherichia coli as the standard template (in pMD19-T vector). This quantification was performed in triplicate technical replicates. Simultaneously, the amplicon sequencing data allowed us to calculate the proportions of each ASV relative to all bacteria. By multiplying these proportions by the total copy numbers, we obtained the copy number density of 16S rRNA (cDNA) and rDNA for each dominant ASV in each treatment. We excluded ASVs with insufficient abundance that could potentially cause bias (retaining those ≥0.1% in all rDNA samples, which were normalized to 20,000 reads). To determine the rRNA copies per cell for these dominant ASVs, the rDNA copy number for each ASV was normalized by the number of genomic ribosomal RNA operons ( rrn ) obtained from the rrn DB . The resulting rRNA copies per cell for both cultivable and uncultivable ASVs were used as indicators of their metabolic activity. We compared these values between cultivable and uncultivable ASVs, as well as their dynamics under four treatments (UI, DI, UR, and DR) relative to the original condition (O), to assess their short-term adaptability. 

To decipher the potential metabolic features of bacterial taxa defined as cultivable and uncultivable under examined cultivation conditions, and then to infer strategies for improving cultivation, their genomes were obtained through genome-centered metagenomic sequencing. We performed metagenomic sequencing on 6 inoculum samples and 17 agar plate samples derived from AS (detailed in Supplementary Data ) using the HiseqX10 sequencing platform (Illumina, US) with a PE150 sequencing strategy (commercial service by Novogene, China). These 17 samples were selected because their 16S rRNA gene profiles exhibited high alpha and beta diversity, and they contained novel taxa with higher relative abundance than others, which allowed for a greater likelihood of obtaining MAGs with taxonomic diversity and novelty. Following quality-control filtering, the metagenomic reads were assembled using MEGAHIT v1.2.9 . Genome binning and primary dereplication were accomplished by utilizing metaWRAP v1.2.2 and dRep v3.3.1 , respectively. The genomic quality was evaluated using CheckM v1.0.12, leading to the exclusion of genomes with less than 50% completeness or over 10% contamination . To reduce redundancy of metagenome-assembled genomes (MAGs) from different cultivation conditions, those exhibiting an average nucleotide identity (ANI) of over 99% from the same sample source were further dereplicated. The one with highest quality (completeness-5×contamination) were retained. Taxonomic annotation of the MAGs was conducted utilizing GTDB-Tk v2.3.0 with GTDB-r214 . Open reading frames were predicted using Prodigal v2.6.3 , and functional annotation as clusters of orthologous groups (COGs) was carried out using the eggNOG-mapper v2 . 

To categorize MAGs into cultivable or uncultivable phenotypes (Supplementary Data ), the following steps were carried out. MAGs obtained from 12 agar plate samples (with corresponding inoculum samples) were designated as cultivable phenotypes. However, MAGs derived from 6 inoculum samples could not be directly defined as uncultivable ones due to two reasons. First, highly similar MAGs (ANI > 99%) could be obtained from both cultivation samples and inoculum samples. Second, MAGs represented only about 40% of the reads (ranging from 14 to 70%) from each agar plate metagenome. The remaining reads, which are also from cultivable taxa, might have matched MAGs that were obtained in the inoculum metagenomes. Therefore, uncultivable MAGs were defined as follows: If selected marker genes (DNA gyrase subunit B and RNA polymerase subunit B) of a MAG extracted from inoculum samples were absent or only detected in the agar plate metagenomes with <1× coverage per 10 GB (based on read mapping and the average of the two genes), these MAGs were defined as uncultivable. To mitigate potential false signals arising from the separated phylogeny of cultivated and uncultivated MAGs, we focused our further investigation on the largest order in our genome catalog, Burkholderiales , which comprised 111 cultivable and 29 uncultivable genomes. This order was widely and dominantly distributed in global wastewater treatment plants , . We conducted a comparative statistical analysis of the presence/absence frequency of COGs (requiring presence in >5% MAGs) for cultivable and uncultivable MAGs within the order. We examined the COGs that were enriched in either group ( P < 0.05, Fisher’s exact test). Based on the functional annotation of these COGs, our objective was to deduce potential conditions related to uncultivability. Of particular interest was the significant underrepresentation of six vitamin B 12 synthesis genes in the uncultivable group compared with the cultivable group. This observation prompted us to conduct cultivation experiments to validate whether the addition of vitamin B 12 could promote the cultivable diversity of this order. For the validation experiments, we used R2A medium and compared the outcomes with or without the addition of 5 μg/L of vitamin B 12 . It is worth noting that R2A medium contained yeast extract, which may have contained approximately 0.5 μg/L of vitamin B 12 , according to Tao et al. . Meanwhile, it was reported that different microorganisms within a community exhibited selectivity for different subtypes of vitamin B 12 , but high concentrations of vitamin B 12 could reduce or even eliminate subtype specificity . The cultivation experiments were conducted on two AS samples (XM4 and ZZ; see Supplementary Data ). Colonies from the agar plates were collected for sequencing and analysis. Due to differences between the taxonomic annotation systems of 16S rRNA genes and the GTDB-Tk system (where Rhodocyclaceae is a family within Burkholderiales ), we assessed the richness and relative abundance of ASVs affiliated with both Burkholderiales and Rhodocyclales (referring to the RDP database) in different treatments. Both orders exhibited high diversity in the original and culturomics samples. 

The R package dplyr was used for data manipulation . Plots were generated using R packages ggplot2 and pheatmap , . 

Further information on research design is available in the linked to this article. 

Supplementary Information Description of Additional Supplementary Files Supplementary Data 1-4 Reporting Summary Transparent Peer Review file 

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