scholarly journals Statistical Transfer Learning with Generative Encoding for Spatial Transcriptomics

2021 ◽  
Author(s):  
David Banh
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Expression Vectors ◽  
Reference Dataset ◽  
Clinical Specimens ◽  
Transcriptomic Level ◽  
Generative Encoding ◽  
Predict Gene Expression

A new method proposes to align single cell reference datasets to spatial tran- scriptomic genes and tissue images. The technique can transfer single cells to unmeasured histology tissue by first aligning a single cell reference dataset to known Spatial Transcriptomic tissue, and learn from the alignment to predict gene expression on new histology. The model can invert the alignment transformation to generate new histology images from gene expression vectors, allowing for in-silico perturbation analyses through dynamically altering the levels of gene expression. Leveraging the cell atlas can lead to annotation of pathology and clinical specimens, enabling a mapping from the cellular and transcriptomic level to imaging tissue.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Extensive Regulation of Metabolism and Growth during the Cell Division Cycle

2014 ◽  
Author(s):  
Nikolai Slavov ◽  
David Botstein ◽  
Amy Caudy
Keyword(s):  
Gene Expression ◽  
Oxygen Consumption ◽  
Cell Division ◽  
Single Cell ◽  
Single Cells ◽  
Emergent Behavior ◽  
Yeast Cells ◽  
Physiological Data ◽  
Synchronized Cultures ◽  
Metabolic Cycle

Yeast cells grown in culture can spontaneously synchronize their respiration, metabolism, gene expression and cell division. Such metabolic oscillations in synchronized cultures reflect single-cell oscillations, but the relationship between the oscillations in single cells and synchronized cultures is poorly understood. To understand this relationship and the coordination between metabolism and cell division, we collected and analyzed DNA-content, gene-expression and physiological data, at hundreds of time-points, from cultures metabolically-synchronized at different growth rates, carbon sources and biomass densities. The data enabled us to extend and generalize our mechanistic model, based on ensemble average over phases (EAP), connecting the population-average gene-expression of asynchronous cultures to the gene-expression dynamics in the single-cells comprising the cultures. The extended model explains the carbon-source specific growth-rate responses of hundreds of genes. Our physiological data demonstrate that the frequency of metabolic cycling in synchronized cultures increases with the biomass density, suggesting that this cycling is an emergent behavior, resulting from the entraining of the single-cell metabolic cycle by a quorum-sensing mechanism, and thus underscoring the difference between metabolic cycling in single cells and in synchronized cultures. Measurements of constant levels of residual glucose across metabolically synchronized cultures indicate that storage carbohydrates are required to fuel not only the G1/S transition of the division cycle but also the metabolic cycle. Despite the large variation in profiled conditions and in the scale of their dynamics, most genes preserve invariant dynamics of coordination with each other and with the rate of oxygen consumption. Similarly, the G1/S transition always occurs at the beginning, middle or end of the high oxygen consumption phases, analogous to observations in human and drosophila cells. These results highlight evolutionary conserved coordination among metabolism, cell growth and division.

scholarly journals ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

scholarly journals Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

2020 ◽  
Vol 117 (46) ◽  
pp. 28784-28794
Author(s):  
Sisi Chen ◽  
Paul Rivaud ◽  
Jong H. Park ◽  
Tiffany Tsou ◽  
Emeric Charles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Probabilistic Models ◽  
Single Cells ◽  
Measurement Techniques ◽  
Patient Specific ◽  
Cell Populations ◽  
Cell Measurement ◽  
The Impact

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

Effector T-cell cytolytic activity modules derived from CD3+ single cells from human primary triple-negative breast cancer (TNBC) in multiple solid tumors to predict response to immune checkpoint blockade therapy (ICB).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 1073-1073
Author(s):  
Chaitanya Ramanuj Acharya ◽  
Herbert K Lyerly
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Single Cells ◽  
Progression Free Survival ◽  
Cytolytic Activity ◽  
Enrichment Score ◽  
P Value ◽  
Free Survival ◽  
Tumor Types

1073 Background: The prognostic and predictive value of tumor infiltrating lymphocytes for ICB has been recognized in a variety of tumor types, including TNBC. Nonetheless, our understanding of the mechanistic aspects of T cell activation remains incomplete. We hypothesize that a specific effector phenotype of T cell cytolytic activity (ECA) is a consistent feature of epithelial tumors, possibly varying by tumor types with a range of inflammatory features. Methods: We evaluated 6,311 purified CD3+ single cells from human primary TNBC and computed sample set enrichment scores of a set of previously published immune metagenes. Following unsupervised clustering of the enrichment scores of the entire single cell population, two subgroups of cells with highest and lowest average enrichment score of T cell cytolytic activity formed a basis for detecting functional gene expression modules. Spectral decomposition and Jackstraw analysis estimated eight modules with overlapping sets of genes. Each gene expression module was then used to train a Random Forest classifier of ECA phenotype. Results: We discovered that our module-derived classifiers were prognostic not only in TNBC samples obtained from both TCGA (N = 150) and METABRIC (N = 320) datasets but also in 14 other tumor types encompassing 6,000 samples. For example, patient samples from TCGA dataset predicted to be in group ECA ‘High’ have better progression-free survival (p-value: 0.0098l; HR: 0.30) and better overall survival (p-value: 0.0066; HR: 0.17). In both breast datasets, gene within the classifier are relatively under-expressed in ER+ tumors as opposed to HER2+ and TNBC (p-value < 2.2e-16). In a dataset of normal, pure DCIS and mixed DCIS (GSE26304;N = 114), the same genes were relatively under-expressed in DCIS samples relative to invasive tumors (p-value < 2.2e-16). Additionally, in a pre-therapy tumor dataset of fifty-one advanced melanoma patients treated with Nivolumab, who previously either progressed on ipilimumab or were ipilimumab-naïve, our module-derived classifier was able to classify responders and non-responders with 77% accuracy (p-value = 0.02) and was associated with progression-free survival (p-value = 0.03; HR: 0.28). Conclusions: Our study highlights one important application of single-cell genomics in our understanding of immune microenvironment and potentially identify new immunotherapy targets.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

2019 ◽  
Author(s):  
Arnav Moudgil ◽  
Michael N. Wilkinson ◽  
Xuhua Chen ◽  
June He ◽  
Alex J. Cammack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Binding Sites ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

scholarly journals SHERRY2: A method for rapid and sensitive single cell RNA-seq

2021 ◽  
Author(s):  
Lin Di ◽  
Bo Liu ◽  
Yuzhu Lyu ◽  
Shihui Zhao ◽  
Yuhong Pang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Dynamic Range ◽  
Single Cells ◽  
Rna Seq ◽  
Wide Dynamic Range ◽  
Uniform Coverage ◽  
Optimized Protocol ◽  
Tn5 Transposase ◽  
Higher Sensitivity

Many single cell RNA-seq applications aim to probe a wide dynamic range of gene expression, but most of them are still challenging to accurately quantify low-aboundance transcripts. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, an optimized protocol for sequencing transcriptomes of single cells or single nuclei. SHERRY2 is robust and scalable, and it has higher sensitivity and more uniform coverage in comparison with prevalent scRNA-seq methods. With throughput of a few thousand cells per batch, SHERRY2 can reveal the subtle transcriptomic differences between cells and facilitate important biological discoveries.

scholarly journals ID: 1027 High-Throughput, Single Cell Whole Transcriptome Sequencing Analysis of Cancer Cells with the New BD FACSMelody™ Cell Sorter and BD™ Precise assay

2017 ◽  
Vol 4 (S) ◽  
pp. 102
Author(s):  
Xiaoyang (Alice) Wang ◽  
Chip Lomas ◽  
Craig Betts ◽  
Aaron Walker ◽  
Christina Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Line ◽  
Selection Criteria ◽  
Single Cells ◽  
Principal Component ◽  
Jurkat Cells ◽  
Sequencing Analysis ◽  
Whole Transcriptome

Gene expression studies performed on bulk samples might obscure the understanding of complex samples. Gene expression analyses performed on single cells, however, can offer a powerful method to resolve sample heterogeneity and reveal hidden biology. Optimal sample preparation is critical to obtain high quality gene expression data from single cells.Historically, single cells or small numbers of cells were isolated and prepared by limiting dilutions, laser capture microdissection, or microfluidics technologies, or fluorescence-activated cell sorting (FACS). FACS sorting enables highthroughput processing of a heterogeneous mixture of cells and ensures the delivery of single cells or a small number ofcells into a chosen receptacle to meet the selection criteria at a purity level that is unmatched by other approaches.Furthermore, by FACS, the single cell selection criteria can be based on surface marker expression, cell size, and granularity(represented by scatter). Sorted cells can be used for any downstream application including next generation sequencing(NGS).In this study, the new, easy-to-use BD FACSMelody™ sorter was applied to sort individual cancer cells. Jurkat cells (a Tleukemia cell line), and T47D cells (a breast cancer cell line) were mixed, stained, analyzed, and sorted on a BD FACSMelody system. The individual cell’s whole transcriptome was interrogated using BD™ Precise Single Cell WTA (whole transcriptome amplification) Assay. Principal component analysis was applied to cluster the sorted Jurkat and T47D-cell populations.

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