Dynamic motility selection drives population segregation in a bacterial swarm

Population expansion in space, or range expansion, is widespread in nature and in clinical settings. Space competition among heterogeneous subpopulations during range expansion is essential to population ecology, and it may involve the interplay of multiple factors, primarily growth and motility of individuals. Structured microbial communities provide model systems to study space competition during range expansion. Here we use bacterial swarms to investigate how single-cell motility contributes to space competition among heterogeneous bacterial populations during range expansion. Our results revealed that motility heterogeneity can promote the spatial segregation of subpopulations via a dynamic motility selection process. The dynamic motility selection is enabled by speed-dependent persistence time bias of single-cell motion, which presumably arises from physical interaction between cells in a densely packed swarm. We further showed that the dynamic motility selection may contribute to collective drug tolerance of swarming colonies by segregating subpopulations with transient drug tolerance to the colony edge. Our results illustrate that motility heterogeneity, or “motility fitness,” can play a greater role than growth rate fitness in determining the short-term spatial structure of expanding populations.

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Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

10.1101/866244 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sydney B. Blattman ◽

Wenyan Jiang ◽

Panos Oikonomou ◽

Saeed Tavazoie

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Low Cost ◽

Bacterial Cells ◽

Single Experiment ◽

Bacterial Populations ◽

Single Cell Rna Sequencing ◽

Gene Expression Heterogeneity ◽

Mrna Polyadenylation

AbstractDespite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. 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 indexing 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 >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

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A mechanism for migrating bacterial populations to non-genetically adapt to new environments

10.1101/2021.09.21.461202 ◽

2021 ◽

Author(s):

Henry H Mattingly ◽

Thierry Emonet

Keyword(s):

Collective Behavior ◽

Group Composition ◽

Phenotypic Diversity ◽

Population Expansion ◽

Migration Speed ◽

Diverse Population ◽

Genetic Inheritance ◽

Bacterial Populations ◽

Physical Environments ◽

Varying Environments

Populations of chemotactic bacteria can rapidly expand into new territory by consuming and chasing an attractant cue in the environment, increasing the population's overall growth in nutrient-rich environments. Although the migrating fronts driving this expansion contain cells of multiple swimming phenotypes, the consequences of non-genetic diversity for population expansion are unknown. Here, through theory and simulations, we predict that expanding populations non-genetically adapt their phenotype composition to migrate effectively through multiple physical environments. Swimming phenotypes in the migrating front are spatially sorted by chemotactic performance, but the mapping from phenotype to performance depends on the environment. Therefore, phenotypes that perform poorly localize to the back of the group, causing them to selectively fall behind. Over cell divisions, the group composition dynamically enriches for high-performers, enhancing migration speed and overall growth. Furthermore, non-genetic inheritance controls a trade-off between large composition shifts and slow responsiveness to new environments, enabling a diverse population to out-perform a non-diverse one in varying environments. These results demonstrate that phenotypic diversity and collective behavior can synergize to produce emergent functionalities. Non-genetic inheritance may generically enable bacterial populations to transiently adapt to new situations without mutations, emphasizing that genotype-to-phenotype mappings are dynamic and context-dependent.

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CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

10.1101/2021.05.24.445360 ◽

2021 ◽

Author(s):

Zhengyu Ouyang ◽

Nathanael Bourgeois ◽

Eugenia Lyashenko ◽

Paige Cundiff ◽

Patrick F Cullen ◽

...

Keyword(s):

Single Cell ◽

Induced Pluripotent Stem Cell ◽

Cell Types ◽

Model Systems ◽

Rna Seq ◽

Cell Type ◽

Fine Grained ◽

Single Nucleus ◽

Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

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Maintenance of Yeast Genome Integrity by RecQ Family DNA Helicases

Genes ◽

10.3390/genes11020205 ◽

2020 ◽

Vol 11 (2) ◽

pp. 205 ◽

Cited By ~ 5

Author(s):

Sonia Vidushi Gupta ◽

Kristina Hildegard Schmidt

Keyword(s):

Genome Instability ◽

Dna Helicase ◽

Model Systems ◽

Yeast Cells ◽

Genome Integrity ◽

Yeast Genome ◽

Physical Interaction ◽

Recq Helicase ◽

Recq Helicases ◽

Domain Structures

With roles in DNA repair, recombination, replication and transcription, members of the RecQ DNA helicase family maintain genome integrity from bacteria to mammals. Mutations in human RecQ helicases BLM, WRN and RecQL4 cause incurable disorders characterized by genome instability, increased cancer predisposition and premature adult-onset aging. Yeast cells lacking the RecQ helicase Sgs1 share many of the cellular defects of human cells lacking BLM, including hypersensitivity to DNA damaging agents and replication stress, shortened lifespan, genome instability and mitotic hyper-recombination, making them invaluable model systems for elucidating eukaryotic RecQ helicase function. Yeast and human RecQ helicases have common DNA substrates and domain structures and share similar physical interaction partners. Here, we review the major cellular functions of the yeast RecQ helicases Sgs1 of Saccharomyces cerevisiae and Rqh1 of Schizosaccharomyces pombe and provide an outlook on some of the outstanding questions in the field.

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973. Single-cell RNA Sequencing Analysis of Zika Virus Infection in Human Stem Cell-Derived Cerebral Organoids

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz359.075 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S33-S34

Author(s):

Karen Ocwieja ◽

Alexandra Stanton ◽

Alexsia Richards ◽

Jenna Antonucci ◽

Travis Hughes ◽

...

Keyword(s):

Stem Cell ◽

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Zika Virus ◽

Model Systems ◽

Sequencing Analysis ◽

Human Stem Cell ◽

Zikv Infection ◽

Single Cell Rna Sequencing

Abstract Background The molecular mechanisms underpinning the neurologic and congenital pathologies caused by Zika virus (ZIKV) infection remain poorly understood. One barrier has been the lack of relevant model systems for the developing human brain; however, thanks to advances in the stem cell field, we can now evaluate ZIKV central nervous system infections in human stem cell-derived cerebral organoids which recapitulate complex 3-dimensional neural architecture. Methods We apply Seq-Well—a simple, portable platform for massively parallel single-cell RNA sequencing—to characterize cerebral organoids infected with ZIKV. Using this sequencing method, and published transcriptional profiles, we identify multiple cellular populations in our organoids, including neuroprogenitor cells, intermediate progenitor cells, and terminally differentiated neurons. We detect and quantify host mRNA transcripts and viral RNA with single-cell resolution, defining transcriptional features of uninfected cells and infected cells. Results In this model of the developing brain, we identify preferred tropisms of ZIKV infection and pronounced effects on cell division, differentiation, and death. Our data additionally reveal differences in cellular populations and gene expression within organoids infected by historic and contemporary ZIKV strains from a variety of geographic locations. This finding might help explain phenotypic differences attributed to the viruses, including variable propensity to cause microcephaly. Conclusion Overall, our work provides insight into normal and diseased human brain development, and suggests that both virus replication and host response mechanisms underlie the neuropathology of ZIKV infection. Disclosures All Authors: No reported Disclosures.

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1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2018.297 ◽

2018 ◽

Vol 45 (S3) ◽

pp. S13-S14

Author(s):

VG LeBlanc ◽

D Trinh ◽

M Hughes ◽

I Luthra ◽

D Livingstone ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Tissue Sample ◽

Expression Patterns ◽

Model Systems ◽

Sequencing Data ◽

Tissue Samples ◽

Original Tumour ◽

Current Therapies ◽

Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

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Abstract A122: Molecular barcoding and single cell approaches to investigate drug tolerance in EGFRmutNSCLC

10.1158/1535-7163.targ-19-a122 ◽

2019 ◽

Author(s):

Jennifer L. Cotton ◽

Viveksagar KrishnamurthyRadhakrishna ◽

Julie Chen ◽

Michelle Piquet ◽

Joel Wagner ◽

...

Keyword(s):

Single Cell ◽

Drug Tolerance ◽

Molecular Barcoding

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Abstract PO-100: Expressed molecular barcoding coupled with single cell RNAseq enables a high resolution investigation into the evolution of drug tolerance

10.1158/1538-7445.tumhet2020-po-100 ◽

2020 ◽

Author(s):

Jennifer L. Cotton ◽

Viveksagar Krisnamurthy Radhakrishna ◽

Javier Estrada Diez ◽

David A. Ruddy ◽

Kathleen Sprouffske ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Drug Tolerance ◽

Molecular Barcoding

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STRIPAK, a highly conserved signaling complex, controls multiple eukaryotic cellular and developmental processes and is linked with human diseases

Biological Chemistry ◽

10.1515/hsz-2019-0173 ◽

2019 ◽

Vol 400 (8) ◽

pp. 1005-1022 ◽

Cited By ~ 17

Author(s):

Ulrich Kück ◽

Daria Radchenko ◽

Ines Teichert

Keyword(s):

Protein Complexes ◽

Model Systems ◽

Human Diseases ◽

Physical Interaction ◽

Developmental Processes ◽

Signaling Complex ◽

Macromolecular Assembly ◽

Complex Functions ◽

Key Issues ◽

Eukaryotic Organisms

Abstract The striatin-interacting phosphatases and kinases (STRIPAK) complex is evolutionary highly conserved and has been structurally and functionally described in diverse lower and higher eukaryotes. In recent years, this complex has been biochemically characterized better and further analyses in different model systems have shown that it is also involved in numerous cellular and developmental processes in eukaryotic organisms. Further recent results have shown that the STRIPAK complex functions as a macromolecular assembly communicating through physical interaction with other conserved signaling protein complexes to constitute larger dynamic protein networks. Here, we will provide a comprehensive and up-to-date overview of the architecture, function and regulation of the STRIPAK complex and discuss key issues and future perspectives, linked with human diseases, which may form the basis of further research endeavors in this area. In particular, the investigation of bi-directional interactions between STRIPAK and other signaling pathways should elucidate upstream regulators and downstream targets as fundamental parts of a complex cellular network.

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