scholarly journals Neural Data Visualization for Scalable and Generalizable Single Cell Analysis

2018 ◽  
Author(s):  
Hyunghoon Cho ◽  
Bonnie Berger ◽  
Jian Peng
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Data Sets ◽  
Cell Analysis ◽  
Data Set ◽  
Unseen Data ◽  
Sequencing Experiment ◽  
Cell Expression

SummarySingle-cell RNA sequencing is becoming effective and accessible as emerging technologies push its scale to millions of cells and beyond. Visualizing the landscape of single cell expression has been a fundamental tool in single cell analysis. However, standard methods for visualization, such as t-stochastic neighbor embedding (t-SNE), not only lack scalability to data sets with millions of cells, but also are unable to generalize to new cells, an important ability for transferring knowledge across fast-accumulating data sets. We introduce net-SNE, which trains a neural network to learn a high quality visualization of single cells that newly generalizes to unseen data. While matching the visualization quality of t-SNE on 14 benchmark data sets of varying sizes, from hundreds to 1.3 million cells, net-SNE also effectively positions previously unseen cells, even when an entire subtype is missing from the initial data set or when the new cells are from a different sequencing experiment. Furthermore, given a “reference” visualization, net-SNE can vastly reduce the computational burden of visualizing millions of single cells from multiple days to just a few minutes of runtime. Our work provides a general framework for newly bootstrapping single cell analysis from existing data sets.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

scholarly journals More than efficacy revealed by single-cell analysis of antiviral therapeutics

2019 ◽  
Author(s):  
Wu Liu ◽  
Mehmet U. Caglar ◽  
Zhangming Mao ◽  
Andrew Woodman ◽  
Jamie J. Arnold ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Targets ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
Viral Population ◽  
Cell Analysis ◽  
Toxic Dose ◽  
Time Required

SUMMARYDevelopment of antiviral therapeutics emphasizes minimization of the effective dose and maximization of the toxic dose, first in cell culture and later in animal models. Long-term success of an antiviral therapeutic is determined not only by its efficacy but also by the duration of time required for drug-resistance to evolve. We have developed a microfluidic device comprised of ~6000 wells, with each well containing a microstructure to capture single cells. We have used this device to characterize enterovirus inhibitors with distinct mechanisms of action. In contrast to population methods, single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates reveals not only efficacy but also properties of the members of the viral population most sensitive to the drug, the stage of the lifecycle most affected by the drug, and perhaps even if the drug targets an interaction of the virus with its host.

scholarly journals μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

2020 ◽  
Author(s):  
Tyler N. Chen ◽  
Anushka Gupta ◽  
Mansi Zalavadia ◽  
Aaron M. Streets
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Protein Localization ◽  
Single Cells ◽  
Optical Measurements ◽  
Detection Sensitivity ◽  
High Resolution Imaging ◽  
Cell Analysis ◽  
Resolution Imaging

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

IMMU-51. SINGLE-CELL ANALYSIS OF TUMOR-ASSOCIATED MICROGLIA AND MACROPHAGES FROM HUMAN GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi104-vi104
Author(s):  
Atul Anand ◽  
Rikke Sick Andersen ◽  
Mark Burton ◽  
Dylan Scott Lykke Harwood ◽  
Frantz Rom Poulsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Primary Brain Tumor ◽  
Tumor Type ◽  
Cell Analysis ◽  
Data Set ◽  
Human Glioblastoma ◽  
Glioblastoma Patient ◽  
Single Cell Rna Sequencing ◽  
Therapeutic Problem

Abstract Patients with glioblastoma, the most frequent and malignant primary brain tumor type, have a poor prognosis with a median survival of 14 months. A major therapeutic problem is chemoresistance. In surgically removed glioblastoma tissue, tumor-associated microglia and macrophages (TAMs) constitute up to 30 % of the total cells. TAMs are capable of secreting cytokines, chemokines and growth factors, thereby influencing the tumor microenvironment. However, the existence of different TAM subtypes and their role in glioblastoma is not fully comprehended and rarely considered therapeutically. This could explain why many glioblastoma clinical trials fail despite of promising preclinical results. This project aims to interrogate the existence and characteristics of different TAM subtypes in human glioblastoma biopsies in order to identify novel subpopulations and therapeutic targets. To study the heterogeneity in TAMs, CD11b+ cells were isolated from glioblastoma patient′s tissue, and single-cell RNA sequencing was performed using the 10X Genomics Chromium platform for single-cell generation and an Illumina NovaSeq6000 system for sequencing. We have sequenced TAMs from three glioblastomas and CD11b+ cells from brain tissue adjacent to two brain metastases samples. In the filtered data set of almost 71,000 CD11b+ cells, we were able to identify recently described TAM populations, such as an interferon-induced, a phagocytic, a hypoxic and a proliferating subset. Interestingly, we also discovered potential novel TAM subsets, such as a pro-angiogenic subset. We have detected a TAM population which is more complex than the established M1 and M2 phenotypes, constituting novel TAM subsets. We are currently investigating these findings to validate specific markers associated with these subpopulations, and for the identification of novel clinically relevant targets.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

The Analyst ◽  
2019 ◽  
Vol 144 (10) ◽  
pp. 3226-3238 ◽  
Author(s):  
Jitraporn Vongsvivut ◽  
David Pérez-Guaita ◽  
Bayden R. Wood ◽  
Philip Heraud ◽  
Karina Khambatta ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Spatial Resolution ◽  
Environmental Science ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Chemical Mapping ◽  
Ftir Microspectroscopy ◽  
Resolution Single

Coupling synchrotron IR beam to an ATR element enhances spatial resolution suited for high-resolution single cell analysis in biology, medicine and environmental science.

scholarly journals Expression Profiling of Single Mammalian Cells – Small is Beautiful

Yeast ◽  
2000 ◽  
Vol 1 (3) ◽  
pp. 211-217 ◽  
Author(s):  
Gerard Brady
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Mammalian Cells ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Small Samples ◽  
Biological Research ◽  
Cell Expression

Increasingly mRNA expression patterns established using a variety of molecular technologies such as cDNA microarrays, SAGE and cDNA display are being used to identify potential regulatory genes and as a means of providing valuable insights into the biological status of the starting sample. Until recently, the application of these techniques has been limited to mRNA isolated from millions or, at very best, several thousand cells thereby restricting the study of small samples and complex tissues. To overcome this limitation a variety of amplification approaches have been developed which are capable of broadly evaluating mRNA expression patterns in single cells. This review will describe approaches that have been employed to examine global gene expression patterns either in small numbers of cells or, wherever possible, in actual isolated single cells. The first half of the review will summarize the technical aspects of methods developed for single-cell analysis and the latter half of the review will describe the areas of biological research that have benefited from single-cell expression analysis.

scholarly journals Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

2017 ◽  
Vol 49 (9) ◽  
pp. 491-495
Author(s):  
Hilary A. Coller
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Experimental Biology ◽  
Intratumor Heterogeneity ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Critical Issues ◽  
Cell To Cell Variability ◽  
External Stimuli

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.

scholarly journals Single cell analysis of lincRNA expression during human blastocyst differentiation identifies TERT(+) multi-lineage precursor cells

2016 ◽  
Author(s):  
Jens Durruthy-Durruthy ◽  
Mark Wossidlo ◽  
Vittorio Sebastiano ◽  
Gennadi Glinsky
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Single Cell Analysis ◽  
Precursor Cells ◽  
Human Embryos ◽  
Cell Analysis ◽  
Embryonic Cells ◽  
Human Blastocyst ◽  
Extinction Events ◽  
Cell Expression

SummaryChromosome instability and aneuploidies occur very frequently in human embryos, impairing proper embryogenesis and leading to cell cycle arrest, loss of cell viability, and developmental failures in 50-80% of cleavage-stage embryos. This high frequency of cellular extinction events represents a significant experimental obstacle challenging analyses of individual cells isolated from human preimplantation embryos. Here, we carried out single cell expression profiling analyses of 241 individual cells recovered from 32 human embryos during the early and late stages of viable human blastocyst differentiation. Classification of embryonic cells was performed solely based on expression patterns of human pluripotency-associated transcripts (HPAT), which represent a family of transposable element-derived lincRNAs highly expressed in human embryonic stem cells (hESCs) and regulating nuclear reprogramming and pluripotency induction. We then validated our findings by analyzing 1,708 individual embryonic cells recovered from more than 100 human embryos and 259 mouse embryonic cells at different stages of preimplantation embryogenesis. Our experiments demonstrate that segregation of human blastocyst cells into distinct sub-populations based on single-cell expression profiling of just three HPATs (HPAT-21; -2; and -15) appears to inform key molecular and cellular events of naïve pluripotency induction and accurately captures a full spectrum of cellular diversity during human blastocyst differentiation. HPAT’s expression-guided spatiotemporal reconstruction of human embryonic development inferred from single-cell expression analysis of viable blastocyst differentiation enabled identification of TERT(+) sub-populations, which are significantly enriched for cells expressing key naïve pluripotency regulatory genes and genetic markers of all three major lineages created during human blastocyst differentiation. Results of our analyses suggest that during early stages of preimplantation embryogenesis putative immortal multi-lineage precursor cells (iMPCs) are created, which then differentiate into trophectoderm, primitive endoderm and pluripotent epiblast lineages. We propose that cellular extinction events in cleavage-stage embryos are triggered by premature activation of HPAT lincRNAs reflecting failed iMPC’s creation attempts.HighlightsSingle cell analysis of 1,949 human & 259 mouse embryonic cellsIdentification of 5 most abundant HPAT lincRNAs in viable human blastocystsExpression profiling of just 3 lincRNAs captures cellular diversity of human blastocystsIdentification & characterization of TERT(+) multi-lineage precursor cellsMTTH/HPAT lincRNAs regulatory axis of naïve pluripotency induction in vivo

scholarly journals CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

2019 ◽  
Author(s):  
Thomas D. Sherman ◽  
Tiger Gao ◽  
Elana J. Fertig
Keyword(s):  
Single Cell ◽  
Data Structures ◽  
Computational Efficiency ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Sparse Data ◽  
Data Sets ◽  
Cell Analysis ◽  
Gradient Based ◽  
Cell Data

AbstractMotivationBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.ResultsWe upgraded CoGAPS in Version 3 to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This software includes a new parallelization framework that is designed around the sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Altogether, these updates to CoGAPS enhance the efficiency of the algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.AvailabilityCoGAPS is available as a Bioconductor package and the source code is provided at github.com/FertigLab/CoGAPS. All efficiency updates to enable single-cell analysis available as of version [email protected]

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