Science Highlights Microbial Single-Cell Transcriptomics: Unlocking New Horizons with M20 Genomics
Microbes rarely exist in isolation; they form dynamic, interacting communities that profoundly influence host development, metabolism, and immunity. While microbiome research has traditionally emphasized species- or community-level interactions, it is now clear that even genetically identical microbial cells may exhibit substantial transcriptional heterogeneity. Understanding this cell-to-cell variation is essential for elucidating microbial adaptation, specialization, and responses to host environments or antibiotics.
A Landmark Science Review
A recent Science review, Dissecting Microbial Communities with Single-Cell Transcriptome Analysis1, underscores the transformative role of single-cell transcriptomics in microbiome research. These technologies are redefining our understanding of microbiome function and dynamics — from uncovering transcriptional heterogeneity and antibiotic responses to characterizing mobile genetic elements within both simple biofilms and complex multispecies ecosystems.
Overcoming the Barriers
Single-cell transcriptomics has revolutionized studies of eukaryotic heterogeneity, but its application to microbes has long been constrained. Microbial cells are smaller, encased by rigid walls, and contain only 1/100–1/1000 the RNA of eukaryotic cells, with transcripts that decay within hours. These inherent challenges limited our ability to probe microbial heterogeneity at single-cell resolution.
To overcome these barriers, researchers have developed innovative strategies. Broadly, microbial single-cell transcriptomic methods can be divided into two categories: scRNA-seq–based approaches, which capture transcriptomes from individual cells, and FISH-based imaging approaches, which visualize transcriptional activity in situ 1(Figure 1).
Figure 1. Principles of microbial single-cell transcriptomics technologies 1.
smRandom-seq: Recognized Innovation
The review highlights smRandom-seq as the first scRNA-seq method applied to the human microbiome, reported in Protein & Cell in 2024. In that study, researchers used smRandom-seq to map single-cell transcriptomic landscapes of human gut microbes (Figure 2), revealing transcriptional heterogeneity in succinate metabolism and the activity of mobile genetic elements in Phascolarctobacterium succinatutens2. The Science review further emphasizes smRandom-seq’s high per-cell transcript capture efficiency, establishing a new benchmark for microbial single-cell transcriptome technologies.
Figure 2. Bacterial gene expression landscape in a human gut microbiome. (a) Bacteria proportion of each genus identified with the smRandom-seq. (b) UMAP plot of the mirobe cells and clusters colored by genus taxonomic annotation using MIC-anno.
From Innovation to Practice: The VITA Platform
As the main developer of smRandom-seq, M20 Genomics rapidly translated this original innovation into practice through its integration into the VITA single-cell transcriptome platform, launching the world’s first commercial microbial single-cell transcriptome product in early 2022. Since then, the VITA platform has been applied to thousands of microbial samples worldwide, delivering stable, reproducible, and high-quality results across a wide range of studies.
- Antibiotic Resistance
In a paper published in Nature Communications (2023), smRandom-seq was applied to characterize antibiotic resistance in E. coli, capturing rare resistant subpopulations and mapping stress-response states undetectable with bulk methods3 (Figure 3).
Figure 3. smRandom-seq identified subpopulations reacting differently to ampicillin. E. coli were treated with ampicillin for 0, 1, 2, and 4 h. UMAP projection of all the cells collected at the different time points, based on their gene expression colored by time points (a) or sub-clusters (b).
- Host–Microbe Interactions
Host–microbe interactions represent another frontier where single-cell resolution is transformative. In a study published in eLife (2024), the VITA platform enabled the simultaneous profiling of host cells and colonizing microbes, uncovering mechanisms of infection, commensalism, and immune modulation4 (Figure 4). Building on this, research published in Advanced Science (2025) extended the approach to explore the three-way interplay among host cells, commensals, and pathogens5 (Figure 5), offering fresh perspectives on how microbial communities shape health and disease.
Figure 4. Pathogenicity heterogeneity of S. marcescens. (a) Joint UMAP two-dimensional analysis showing that are distinct clusters among S. marcescens alone, with force and with larvae. (b) The cell subpopulation among the control, force, and larvae groups. There were three distinct subpopulations in the control and force groups. (c) Mean expression levels of genes involved in ABC transporter, quorum sensing, secretion system, two-component system, LPS and peptidoglycan biosynthesis, and virulence-related genes in different subclusters. The shape of each dot indicates the proportion of cells in the cluster, while the color indicates the average activity normalized from 0% to 100% across all clusters.
Figure 5. Larvae and L. plantarum induced phenotypic heterogeneity of S. marcescens. (a) UMAP projection of all S. marcescens collected in S, SF, LFS, and LS groups, based on their gene expression colored by each group. (b) UMAP 2D representation of the 15 cell subclusters from all S. marcescens collected in S, SF, LFS, and LS groups. (c) The proportion of cell lineages of S. marcescens in S, LS, SF, and LFS groups. The colors correspond to the different cluster types. (d) Mean expression levels and proportions of glycolysis/gluconeogenesis genes in different subclusters. The shape of each dot indicates the proportion of cells in the cluster, while the color indicates the average activity normalized from 0% to 100% across all clusters. (e)Expression levels and proportions of genes in TCA cycle genes in different subcluster.
- Functional Redundancy and Heterogeneity
Functional redundancy and heterogeneity are defining features of microbial populations, yet their systematic analysis has long been constrained by traditional approaches. Bulk metagenomics and metatranscriptomics provide community-level averages, but they inevitably mask the cell-to-cell variability and the overlapping metabolic functions that occur both across species and within species-level subgroups. In a study published in Nature Microbiology (2024), researchers analyzed 33 bovine rumen samples using the VITA platform6. The study revealed overlapping metabolic functions across species and within subgroups (Figure 6), offering unprecedented insights into both functional redundancy and intra-population heterogeneity in microbial ecosystems. Furthermore, in another paper published in iMeta (2025), researchers applied the VITA platform to fecal samples of different enterotypes and discovered that the same bacterial species can play distinct functional roles in different gut types7, highlighting the unique ability of single-cell transcriptomics to dissect functional heterogeneity and context-dependent functional shifts at single-cell resolution (Figure 7).
Figure 6. Functional gene proportions (FGPs) change and species of four clusters in the four continuous steps in the metabolization of pectin to produce pyruvate. Top: The FGPs of the four clusters at each step. Bottom: The species composition of the four clusters.
Figure 7. Sankey plot illustrating the functional distribution of species-specific marker genes across three enterotype samples.
Looking Ahead
The Science review reaffirms what our collaborators and customers are already experiencing: single-cell transcriptomics is opening a new era in microbiome research. With the VITA platform powered by smRandom-seq, M20 Genomics remains committed to original innovation, enabling researchers worldwide to uncover the hidden complexity of microbial communities and driving forward discoveries in health, disease, and biotechnology.
Reference:
1. Pountain, A. W. & Yanai, I. Dissecting microbial communities with single-cell transcriptome analysis. Science 389, (2025).
2. Shen, Y. et al. High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome. Protein Cell 1–16 (2024). doi:10.1093/procel/pwae027
3. Xu, Z. et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat. Commun. 14, 1–12 (2023).
4. Wang, Z. et al. Hosts manipulate lifestyle switch and pathogenicity heterogeneity of opportunistic pathogens in the single-cell resolution. Elife 13, 1–25 (2024).
5. Zhang, S. et al. Hosts and Commensal Bacteria Synergistically Antagonize Opportunistic Pathogens at the Single‐Cell Resolution. Adv. Sci. 00582, 1–14 (2025).
6. Jia, M. et al. Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome. Nat. Microbiol. 9, 1884–1898 (2024).
7. Shen, Y. et al. Single‐microbe RNA sequencing uncovers unexplored specialized metabolic functions of keystone species in the human gut. iMeta 4, 1–25 (2025).
