 
@Article{Blattman2020,
  author    = {Blattman, Sydney B. and Jiang, Wenyan and Oikonomou, Panos and Tavazoie, Saeed},
  journal   = {Nature Microbiology},
  title     = {Prokaryotic single-cell {RNA} sequencing by in situ combinatorial indexing},
  year      = {2020},
  issn      = {2058-5276},
  month     = oct,
  number    = {10},
  pages     = {1192--1201},
  volume    = {5},
  abstract  = {Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems1–13, the application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance14–16, lack of mRNA polyadenylation and thick cell walls17. Here, we present prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)—a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing11,12,18 to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single-cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates of more than 200 mRNAs per cell for exponentially growing Escherichia coli. These characteristics enable robust discrimination of cell states corresponding to different phases of growth. When applied to wild-type Staphylococcus aureus, PETRI-seq revealed a rare subpopulation of cells undergoing prophage induction. We anticipate that PETRI-seq will have broad utility in defining single-cell states and their dynamics in complex microbial communities.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  doi       = {10.1038/s41564-020-0729-6},
  keywords  = {Applied microbiology, Gene expression analysis, Systems analysis, Transcriptomics},
  language  = {en},
  publisher = {Nature Publishing Group},
  url       = {https://www.nature.com/articles/s41564-020-0729-6},
  urldate   = {2024-12-12},
}

@Article{Blattman2024,
  author    = {Blattman, Sydney B. and Jiang, Wenyan and McGarrigle, E. Riley and Liu, Menghan and Oikonomou, Panos and Tavazoie, Saeed},
  journal   = {Nature},
  title     = {Identification and genetic dissection of convergent persister cell states},
  year      = {2024},
  issn      = {1476-4687},
  month     = dec,
  number    = {8042},
  pages     = {438--446},
  volume    = {636},
  abstract  = {Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections1. However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease4, and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections.},
  copyright = {2024 The Author(s)},
  doi       = {10.1038/s41586-024-08124-2},
  file      = {Full Text PDF:Blattman2024 - Identification and Genetic Dissection of Convergent Persister Cell States.pdf:PDF:https\://www.nature.com/articles/s41586-024-08124-2.pdf},
  keywords  = {Antimicrobial resistance, Bacterial genetics, Functional genomics, Gene expression, Transcriptomics},
  language  = {en},
  publisher = {Nature Publishing Group},
  url       = {https://www.nature.com/articles/s41586-024-08124-2},
  urldate   = {2025-01-08},
}

@Article{Kuijpers2024,
  author   = {Kuijpers, Lucas and Hornung, Bastian and van den Hout - van Vroonhoven, Mirjam C. G. N. and van IJcken, Wilfred F. J. and Grosveld, Frank and Mulugeta, Eskeatnaf},
  journal  = {BMC Genomics},
  title    = {Split {Pool} {Ligation}-based {Single}-cell {Transcriptome} sequencing ({SPLiT}-seq) data processing pipeline comparison},
  year     = {2024},
  issn     = {1471-2164},
  month    = apr,
  number   = {1},
  pages    = {361},
  volume   = {25},
  abstract = {Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.},
  doi      = {10.1186/s12864-024-10285-3},
  file     = {Full Text PDF:Kuijpers2024 - Split Pool Ligation Based Single Cell Transcriptome Sequencing (SPLiT Seq) Data Processing Pipeline Comparison.pdf:PDF:https\://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-024-10285-3},
  keywords = {SPLiT-seq, Split-pool barcoding, Combinatorial barcoding, Data-preprocessing, Single cell RNA sequencing},
  url      = {https://doi.org/10.1186/s12864-024-10285-3},
  urldate  = {2025-01-08},
}

@Article{Rosenberg2018,
  author    = {Rosenberg, Alexander B. and Roco, Charles M. and Muscat, Richard A. and Kuchina, Anna and Sample, Paul and Yao, Zizhen and Graybuck, Lucas T. and Peeler, David J. and Mukherjee, Sumit and Chen, Wei and Pun, Suzie H. and Sellers, Drew L. and Tasic, Bosiljka and Seelig, Georg},
  journal   = {Science},
  title     = {Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding},
  year      = {2018},
  month     = apr,
  number    = {6385},
  pages     = {176--182},
  volume    = {360},
  abstract  = {To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.},
  doi       = {10.1126/science.aam8999},
  file      = {Full Text PDF:https\://www.science.org/doi/pdf/10.1126/science.aam8999:application/pdf},
  publisher = {American Association for the Advancement of Science},
  url       = {https://www.science.org/doi/10.1126/science.aam8999},
  urldate   = {2025-01-23},
}

 
@Article{Ma2023,
  author   = {Ma, Peijun and Amemiya, Haley M. and He, Lorrie L. and Gandhi, Shivam J. and Nicol, Robert and Bhattacharyya, Roby P. and Smillie, Christopher S. and Hung, Deborah T.},
  journal  = {Cell},
  title    = {Bacterial droplet-based single-cell {RNA}-seq reveals antibiotic-associated heterogeneous cellular states},
  year     = {2023},
  issn     = {0092-8674},
  month    = feb,
  number   = {4},
  pages    = {877--891.e14},
  volume   = {186},
  abstract = {We introduce BacDrop, a highly scalable technology for bacterial single-cell RNA sequencing that has overcome many challenges hindering the development of scRNA-seq in bacteria. BacDrop can be applied to thousands to millions of cells from both gram-negative and gram-positive species. It features universal ribosomal RNA depletion and combinatorial barcodes that enable multiplexing and massively parallel sequencing. We applied BacDrop to study Klebsiella pneumoniae clinical isolates and to elucidate their heterogeneous responses to antibiotic stress. In an unperturbed population presumed to be homogeneous, we found within-population heterogeneity largely driven by the expression of mobile genetic elements that promote the evolution of antibiotic resistance. Under antibiotic perturbation, BacDrop revealed transcriptionally distinct subpopulations associated with different phenotypic outcomes including antibiotic persistence. BacDrop thus can capture cellular states that cannot be detected by bulk RNA-seq, which will unlock new microbiological insights into bacterial responses to perturbations and larger bacterial communities such as the microbiome.},
  doi      = {10.1016/j.cell.2023.01.002},
  file     = {ScienceDirect Full Text PDF:https\://www.sciencedirect.com/science/article/pii/S0092867423000028/pdfft?download=true:application/pdf},
  keywords = {bacterial single-cell RNA-seq, droplet, massively parallel sequencing, antibiotic perturbation, bacterial heterogeneity, antibiotic resistance, antibiotic persistence},
  url      = {https://www.sciencedirect.com/science/article/pii/S0092867423000028},
  urldate  = {2025-01-31},
}

@Article{Macosko2015,
  author   = {Evan Z. Macosko and Anindita Basu and Rahul Satija and James Nemesh and Karthik Shekhar and Melissa Goldman and Itay Tirosh and Allison R. Bialas and Nolan Kamitaki and Emily M. Martersteck and John J. Trombetta and David A. Weitz and Joshua R. Sanes and Alex K. Shalek and Aviv Regev and Steven A. McCarroll},
  journal  = {Cell},
  title    = {Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets},
  year     = {2015},
  issn     = {0092-8674},
  number   = {5},
  pages    = {1202-1214},
  volume   = {161},
  abstract = {Summary
Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell’s RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts’ cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.
Video Abstract},
  doi      = {https://doi.org/10.1016/j.cell.2015.05.002},
  url      = {https://www.sciencedirect.com/science/article/pii/S0092867415005498},
}

@Comment{jabref-meta: databaseType:bibtex;}
