Profiling the heterogeneity of microbial populations and communities at the single-cell level

Abstract

Recent advancements in single-cell genomic and transcriptomic sequencing, in situ sequencing, and molecular imaging-based technologies have facilitated the examination of heterogeneity within microbial communities at the single-cell level. These cutting-edge methodologies permit the capture of phenotypic and genotypic heterogeneity, as well as the spatial organization within microbial communities. This enables in-depth investigation into microbial dark matter, the evaluation of microbial responses to perturbations, and a comprehensive exploration of spatial functions involved in community assembly and social interactions within microbial communities. These interactions include inter-microbial relationships, bacteria–phage interactions, and host–microbe interactions. Here, we highlight the key technological breakthroughs achieved, elucidating the perspectives from which these technologies enable us to interpret microbial heterogeneity at the single-cell level. Additionally, we critically examine the limitations associated with these technologies. Furthermore, we explore how these methods could be combined and also their applications in future studies. The integration of these approaches holds great promise for increasing our understanding of the organization and function of microbes in complex ecosystems.

Keywords

heterogeneity / microbial populations / single-cell level / spatial

References

[1] Payne AC, Chiang ZD, Reginato PL, Mangiameli SM, Murray EM, Yao CC, et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science. 2021; 371:eaay3446.

[2] Ke R, Mignardi M, Pacureanu A, Svedlund J, Botling J, Wählby C, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods. 2013; 10: 857–860.

[3] Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353: 78–82.

[4] Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019; 363: 1463–1467.

[5] Lötstedt B, Stražar M, Xavier R, Regev A, Vickovic S. Spatial host-microbiome sequencing reveals niches in the mouse gut. Nat Biotechnol. 2024; 42: 1394–1403.

[6] Sheth RU, Li M, Jiang W, Sims PA, Leong KW, Wang HH. Spatial metagenomic characterization of microbial biogeography in the gut. Nat Biotechnol. 2019; 37: 877–883.

[7] Moor AE, Harnik Y, Ben-Moshe S, Massasa EE, Rozenberg M, Eilam R, et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell. 2018; 175: 1156–1167.e15.

[8] Nichterwitz S, Chen G, Aguila Benitez J, Yilmaz M, Storvall H, Cao M, et al. Laser capture microscopy coupled with Smart-seq 2 for precise spatial transcriptomic profiling. Nat Commun. 2016; 7:12139.

[9] Rinke C, Lee J, Nath N, Goudeau D, Thompson B, Poulton N, et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat Protoc. 2014; 9: 1038–1048.

[10] Xu L, Brito IL, Alm EJ, Blainey PC. Virtual microfluidics for digital quantification and single-cell sequencing. Nat Methods. 2016; 13: 759–762.

[11] Lan F, Demaree B, Ahmed N, Abate AR. Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat Biotechnol. 2017; 35: 640–646.

[12] Zhang H, Huang C, Li Y, Gupte R, Samuel R, Dai J, et al. FIDELITY: a quality control system for droplet microfluidics. Sci Adv. 2022; 8:eabc9108.

[13] Spits C, Le Caignec C, De Rycke M, Van Haute L, Van Steirteghem A, Liebaers I, et al. Whole-genome multiple displacement amplification from single cells. Nat Protoc. 2006; 1: 1965–1970.

[14] Stepanauskas R, Fergusson EA, Brown J, Poulton NJ, Tupper B, Labonté JM, et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat Commun. 2017; 8: 84.

[15] Raghunathan A, Ferguson, Jr. HR, Bornarth CJ, Song W, Driscoll M, Lasken RS. Genomic DNA amplification from a single bacterium. Appl Environ Microbiol. 2005; 71: 3342–3347.

[16] Podar M, Abulencia CB, Walcher M, Hutchison D, Zengler K, Garcia JA, et al. Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl Environ Microbiol. 2007; 73: 3205–3214.

[17] Zheng W, Zhao S, Yin Y, Zhang H, Needham DM, Evans ED, et al. High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science. 2022; 376:eabm1483.

[18] Zhang J, Su X, Wang Y, Wang X, Zhou S, Jia H, et al. Improved single-cell genome amplification by a high-efficiency phi29 DNA polymerase. Front Bioeng Biotechnol. 2023; 11:1233856.

[19] Telenius H, Carter NP, Bebb CE, Nordenskjöld M, Ponder BAJ, Tunnacliffe A. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics. 1992; 13: 718–725.

[20] Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 2012; 338: 1622–1626.

[21] Langmore JP. Rubicon Genomics, Inc. Pharmacogenomics. 2002; 3: 557–560.

[22] Chen C, Xing D, Tan L, Li H, Zhou G, Huang L, et al. Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science. 2017; 356: 189–194.

[23] Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010; 329: 533–538.

[24] Milo R, Phillips R. Cell biology by the numbers. New York, NY: Garland Science, Taylor & Francis Group; 2016. p. xlii 356

[25] Giannoukos G, Ciulla DM, Huang K, Haas BJ, Izard J, Levin JZ, et al. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 2012; 13: r23.

[26] Homberger C, Hayward RJ, Barquist L, Vogel J. Improved bacterial single-cell RNA-Seq through automated MATQ-Seq and Cas9-based removal of rRNA reads. mBio. 2023; 14:e0355722.

[27] Sheng K, Cao W, Niu Y, Deng Q, Zong C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods. 2017; 14: 267–270.

[28] Homberger C, Saliba AE, Vogel J. A MATQ-seq-based protocol for single-cell RNA-seq in bacteria. Methods Mol Biol. 2023; 2584: 105–121.

[29] Blattman SB, Jiang W, Oikonomou P, Tavazoie S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat Microbiol. 2020; 5: 1192–1201.

[30] Yan X, Liao H, Wang C, Huang C, Zhang W, Guo C, et al. An improved bacterial single-cell RNA-seq reveals biofilm heterogeneity. eLife. 2024; 13:RP97543.

[31] Kuchina A, Brettner LM, Paleologu L, Roco CM, Rosenberg AB, Carignano A, et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science. 2021; 371:eaba5257.

[32] McNulty R, Sritharan D, Pahng SH, Meisch JP, Liu S, Brennan MA, et al. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nat Microbiol. 2023; 8: 934–945.

[33] Ma P, Amemiya HM, He LL, Gandhi SJ, Nicol R, Bhattacharyya RP, et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell. 2023; 186: 877–891.e14.

[34] Xu Z, Wang Y, Sheng K, Rosenthal R, Liu N, Hua X, et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat Commun. 2023; 14: 5130.

[35] Jia M, Zhu S, Xue MY, Chen H, Xu J, Song M, et al. Single-cell transcriptomics across 2534 microbial species reveals functional heterogeneity in the rumen microbiome. Nat Microbiol. 2024; 9: 1884–1898.

[36] Shen Y, Qian Q, Ding L, Qu W, Zhang T, Song M, et al. High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome. Protein Cell. 2025; 16: 211–226.

[37] Shen Y, Qu W, Song M, Zhang T, Liu C, Shi X, et al. Single-microbe RNA sequencing uncovers unexplored specialized metabolic functions of keystone species in the human gut. Imeta. 2025; 4:e70035.

[38] Wang B, Lin AE, Yuan J, Novak KE, Koch MD, Wingreen NS, et al. Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection. Nat Microbiol. 2023; 8: 1846–1862.

[39] Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Ferrante TC, Terry R, et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc. 2015; 10: 442–458.

[40] Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, et al. Highly multiplexed subcellular RNA sequencing in situ. Science. 2014; 343: 1360–1363.

[41] Galeano Niño JL, Wu H, LaCourse KD, Kempchinsky AG, Baryiames A, Barber B, et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature. 2022; 611: 810–817.

[42] Saarenpää S, Shalev O, Ashkenazy H, Carlos V, Lundberg DS, Weigel D, et al. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat Biotechnol. 2024; 42: 1384–1393.

[43] Ludwig W. ARB: a software environment for sequence data. Nucleic Acids Res. 2004; 32: 1363–1371.

[44] Wright ES, Yilmaz LS, Corcoran AM, Ökten HE, Noguera DR. Automated design of probes for rRNA-targeted fluorescence in situ hybridization reveals the advantages of using dual probes for accurate identification. Appl Environ Microbiol. 2014; 80: 5124–5133.

[45] Loy A, Arnold R, Tischler P, Rattei T, Wagner M, Horn M. probeCheck--a central resource for evaluating oligonucleotide probe coverage and specificity. Environ Microbiol. 2008; 10: 2894–2898.

[46] Yilmaz LS, Parnerkar S, Noguera DR. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl Environ Microbiol. 2011; 77: 1118–1122.

[47] Greuter D, Loy A, Horn M, Rattei T. probeBase--an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016. Nucleic Acids Res. 2016; 44: D586–D589.

[48] Nejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science. 2020; 368: 973–980.

[49] Valm AM, Mark Welch JL, Borisy GG. CLASI-FISH: principles of combinatorial labeling and spectral imaging. Syst Appl Microbiol. 2012; 35: 496–502.

[50] Valm AM, Welch JLM, Rieken CW, Hasegawa Y, Sogin ML, Oldenbourg R, et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc Natl Acad Sci USA. 2011; 108: 4152–4157.

[51] Shi H, Shi Q, Grodner B, Lenz JS, Zipfel WR, Brito IL, et al. Highly multiplexed spatial mapping of microbial communities. Nature. 2020; 588: 676–681.

[52] Cao Z, Zuo W, Wang L, Chen J, Qu Z, Jin F, et al. Spatial profiling of microbial communities by sequential FISH with error-robust encoding. Nat Commun. 2023; 14: 1477.

[53] Dar D, Dar N, Cai L, Newman DK. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science. 2021; 373:eabi4882.

[54] Sarfatis A, Wang Y, Twumasi-Ankrah N, Moffitt JR. Highly multiplexed spatial transcriptomics in bacteria. Science. 2025; 387:eadr0932.

[55] Wang T, Shen P, He Y, Zhang Y, Liu J. Spatial transcriptome uncovers rich coordination of metabolism in E. coli K12 biofilm. Nat Chem Biol. 2023; 19: 940–950.

[56] Safieddine A, Coleno E, Lionneton F, Traboulsi AM, Salloum S, Lecellier CH, et al. HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging. Nat Protoc. 2023; 18: 157–187.

[57] Wang W, Lin L, Du Y, Song Y, Peng X, Chen X, et al. Assessing the viability of transplanted gut microbiota by sequential tagging with D-amino acid-based metabolic probes. Nat Commun. 2019; 10: 1317.

[58] Lin L, Wu Q, Song J, Du Y, Gao J, Song Y, et al. Revealing the in vivo growth and division patterns of mouse gut bacteria. Sci Adv. 2020; 6:eabb2531.

[59] Wang W, Zhang N, Du Y, Gao J, Li M, Lin L, et al. Three-dimensional quantitative imaging of native microbiota distribution in the gut. Angew Chem Int Ed. 2021; 60: 3055–3061.

[60] Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D, Dasgupta S, et al. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat Med. 2015; 21: 1091–1100.

[61] Le HH, Lee MT, Besler KR, Comrie JMC, Johnson EL. Characterization of interactions of dietary cholesterol with the murine and human gut microbiome. Nat Microbiol. 2022; 7: 1390–1403.

[62] Carter AM, Woods EC, Bogyo M. Chemical strategies for targeting lipid pathways in bacterial pathogens. Curr Opin Chem Biol. 2025; 86:102596.

[63] Whitaker WR, Shepherd ES, Sonnenburg JL. Tunable expression tools enable single-cell strain distinction in the gut microbiome. Cell. 2017; 169: 538–546.e12.

[64] He Y, Wang X, Ma B, Xu J. Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution. Biotech Adv. 2019; 37:107388.

[65] Wang D, He P, Wang Z, Li G, Majed N, Gu AZ. Advances In single cell Raman spectroscopy technologies for biological and environmental applications. Curr Opin Biotechnol. 2020; 64: 218–229.

[66] Li H, Ding J, Zhu L, Xu F, Li W, Yao Y, et al. Single-cell Raman and functional gene analysis reveals microbial P solubilization in agriculture waste-modified soils. mLife. 2023; 2: 190–200.

[67] He Y, Huang S, Zhang P, Ji Y, Xu J. Intra-Ramanome correlation analysis unveils metabolite conversion network from an isogenic population of cells. mBio. 2021; 12:e0147021.

[68] Berry D, Mader E, Lee TK, Woebken D, Wang Y, Zhu D, et al. Tracking heavy water (D₂O) incorporation for identifying and sorting active microbial cells. Proc Natl Acad Sci USA. 2015; 112: E194–E203.

[69] Jing X, Gou H, Gong Y, Su X, Xu L, Ji Y, et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ Microbiol. 2018; 20: 2241–2255.

[70] Wang X, Ren L, Su Y, Ji Y, Liu Y, Li C, et al. Raman-activated droplet sorting (RADS) for label-free high-throughput screening of microalgal single-cells. Anal Chem. 2017; 89: 12569–12577.

[71] Jing X, Gong Y, Xu T, Meng Y, Han X, Su X, et al. One-cell metabolic phenotyping and sequencing of soil microbiome by Raman-activated gravity-driven encapsulation (RAGE). mSystems. 2021; 6:e0018121.

[72] Lindley M, Gala de Pablo J, Peterson W, Isozaki A, Hiramatsu K, Goda K. High-throughput Raman-activated cell sorting in the fingerprint region. Adv. Mater Technol. 2022; 7:2101567.

[73] Wang X, Xin Y, Ren L, Sun Z, Zhu P, Ji Y, et al. Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo. Sci Adv. 2020; 6:eabb3521.

[74] Zhang J, Ren L, Zhang L, Gong Y, Xu T, Wang X, et al. Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products. Imeta. 2023; 2:e117.

[75] Jing X, Gong Y, Diao Z, Ma Y, Meng Y, Chen J, et al. Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq. Innovation. 2025; 6:100759.

[76] Liu M, Zhu P, Zhang L, Gong Y, Wang C, Sun L, et al. Single-cell identification, drug susceptibility test, and whole-genome sequencing of Helicobacter pylori directly from gastric biopsy by clinical antimicrobial susceptibility test ramanometry. Clin Chem. 2022; 68: 1064–1074.

[77] Zhu P, Ren L, Zhu Y, Dai J, Liu H, Mao Y, et al. Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-R. mLife. 2022; 1: 329–340.

[78] Jing X, Gong Y, Xu T, Davison PA, MacGregor-Chatwin C, Hunter CN, et al. Revealing CO₂-fixing SAR11 bacteria in the ocean by Raman-based single-cell metabolic profiling and genomics. Biodesign Res. 2022; 2022:9782712.

[79] Xu T, Gong Y, Su X, Zhu P, Dai J, Xu J, et al. Phenome-genome profiling of single bacterial cell by Raman-activated gravity-driven encapsulation and sequencing. Small. 2020; 16:e2001172.

[80] Jing X, Gong Y, Pan H, Meng Y, Ren Y, Diao Z, et al. Single-cell Raman-activated sorting and cultivation (scRACS-culture) for assessing and mining in situ phosphate-solubilizing microbes from nature. ISME Commun. 2022; 2: 106.

[81] Casabella S, Scully P, Goddard N, Gardner P. Automated analysis of single cells using laser tweezers Raman spectroscopy. Analyst (Lond). 2016; 141: 689–696.

[82] Zhou B, Sun L, Fang T, Li H, Zhang R, Ye A. Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning. J Biophotonics. 2022; 15:e202100312.

[83] Ho CS, Jean N, Hogan CA, Blackmon L, Jeffrey SS, Holodniy M, et al. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat Commun. 2019; 10: 4927.

[84] Xu J, Yi X, Jin G, Peng D, Fan G, Xu X, et al. High-speed diagnosis of bacterial pathogens at the single cell level by Raman microspectroscopy with machine learning filters and denoising autoencoders. ACS Chem Biol. 2022; 17: 376–385.

[85] Lima C, Muhamadali H, Xu Y, Kansiz M, Goodacre R. Imaging isotopically labeled bacteria at the single-cell level using high-resolution optical infrared photothermal spectroscopy. Anal Chem. 2021; 93: 3082–3088.

[86] Shams S, Lima C, Xu Y, Ahmed S, Goodacre R, Muhamadali H. Optical photothermal infrared spectroscopy: a novel solution for rapid identification of antimicrobial resistance at the single-cell level via deuterium isotope labeling. Front Microbiol. 2023; 14:1077106.

[87] Lima C, Muhamadali H, Goodacre R. Monitoring phenotype heterogeneity at the single-cell level within Bacillus populations producing poly-3-hydroxybutyrate by label-free super-resolution infrared imaging. Anal Chem. 2023; 95: 17733–17740.

[88] Wang Y, Liu H, Geng F, Yang P, Lu J, Li X., et al. Label-free analysis of biofilm phenotypes by infrared micro- and correlation spectroscopy. Anal Bioanal Chem. 2023; 415: 3515–3523.

[89] Ibáñez AJ, Fagerer SR, Schmidt AM, Urban PL, Jefimovs K, Geiger P, et al. Mass spectrometry-based metabolomics of single yeast cells. Proc Natl Acad Sci USA. 2013; 110: 8790–8794.

[90] Foucault ML, Thomas L, Goussard S, Branchini BR, Grillot-Courvalin C. In vivo bioluminescence imaging for the study of intestinal colonization by Escherichia coli in mice. Appl Environ Microbiol. 2010; 76: 264–274.

[91] Wang W, Yang Q, Du Y, Zhou X, Du X, Wu Q, et al. Metabolic labeling of peptidoglycan with NIR-II Dye enables in vivo imaging of gut microbiota. Angew Chem Int Ed. 2020; 59: 2628–2633.

[92] Brunker J, Yao J, Laufer J, Bohndiek SE. Photoacoustic imaging using genetically encoded reporters: a review. J Biomed Opt. 2017; 22:070901.

[93] Li N, Zuo B, Huang S, Zeng B, Han D, Li T, et al. Spatial heterogeneity of bacterial colonization across different gut segments following inter-species microbiota transplantation. Microbiome. 2020; 8: 161.

[94] Celik Ozgen V, Kong W, Blanchard AE, Liu F, Lu T. Spatial interference scale as a determinant of microbial range expansion. Sci Adv. 2018; 4:eaau0695.

[95] Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science. 2014; 344: 416–420.

[96] Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015; 9: 2386–2399.

[97] Yilmaz S, Haroon MF, Rabkin BA, Tyson GW, Hugenholtz P. Fixation-free fluorescence in situ hybridization for targeted enrichment of microbial populations. ISME J. 2010; 4: 1352–1356.

[98] Martinez-Garcia M, Brazel DM, Swan BK, Arnosti C, Chain PSG, Reitenga KG, et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia. PLoS One. 2012; 7:e35314.

[99] Yamaguchi T, Fuchs BM, Amann R, Kawakami S, Kubota K, Hatamoto M, et al. Rapid and sensitive identification of marine bacteria by an improved in situ DNA hybridization chain reaction (quickHCR-FISH). Syst Appl Microbiol. 2015; 38: 400–405.

[100] Wu X, Xu W, Deng L, Li Y, Wang Z, Sun L, et al. Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nat Biomed Eng. 2024; 8: 872–889.

[101] Pett-Ridge J, Weber PK. NanoSIP: NanoSIMS applications for microbial biology. Methods Mol Biol. 2022; 2349: 91–136.

[102] Wang Y, Huang WE, Cui L, Wagner M. Single cell stable isotope probing In microbiology using Raman microspectroscopy. Curr Opin Biotechnol. 2016; 41: 34–42.

[103] Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol. 2025; 26: 11–31.

[104] Hu Y, Wan S, Luo Y, Li Y, Wu T, Deng W, et al. Benchmarking algorithms for single-cell multi-omics prediction and integration. Nat Methods. 2024; 21: 2182–2194.

[105] Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev2024; 16: 57–67.

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