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	Analisi della Diversità del Microbioma a Singola Cella	Analisi RNA-seq a singola cellula
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2019-2022	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Ideatore	Paul Blainey lab and Bhatt lab (pioneered microSPLiT and single-microbe genomics approaches)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Tipo	Computational-experimental omics pipeline	High-throughput single-cell transcriptomic profiling pipeline
Fonte seminale	Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Bhatt, S., ... & Blainey, P. C. (2019). Massively parallel screening of synthetic microbial communities. Proceedings of the National Academy of Sciences, 116(26), 12804-12809.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
Alias	sc-microbiome analysis, single-cell microbial profiling, single-bacterium sequencing, microSPLiT analysis	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Correlati	3	5
Sintesi	Single-cell microbiome diversity analysis resolves the composition and functional heterogeneity of microbial communities at the level of individual cells or bacteria. By combining single-cell or single-bacterium isolation with high-throughput sequencing, this pipeline overcomes the averaging effect of bulk metagenomics, enabling detection of rare strains, intra-species variation, and cell-to-cell heterogeneity within complex microbiomes such as the gut, oral cavity, or environmental samples.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.