scholarly journals Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Maeve O’Huallachain ◽  
Felice-Alessio Bava ◽  
Mary Shen ◽  
Carolina Dallett ◽  
Sri Paladugu ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Model Systems ◽  
Cellular Heterogeneity ◽  
Human Model ◽  
Targeted Analysis ◽  
Technological Advances ◽  
And Function

AbstractSingle-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

scholarly journals Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Tumour Heterogeneity ◽  
Fresh Tissue ◽  
Human Cancers ◽  
Cryopreserved Cell ◽  
Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

High-Throughput Production of Single-Cell Microparticles Using an Inkjet Printing Technology

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Tao Xu ◽  
Helen Kincaid ◽  
Anthony Atala ◽  
James J. Yoo
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Stem Cell Differentiation ◽  
Cell Number ◽  
High Throughput Drug Screening ◽  
Alginate Concentration ◽  
High Throughput Method ◽  
Normal Cellular Function

In this study, a novel biocompatible and inexpensive method for the rapid production of single-cell based microparticles is described. Using an HP DeskJet 550C printer, alginate microparticles containing one to several insulin-producing cells (beta-TC6) were fabricated by coprinting the cells and sodium alginate suspension into a CaCl2 solution. This method is able to generate microparticles of 30–60μm in diameter at a rate as high as 55,000particles∕s. Cell survival assays showed that more than 89% of printed cells survived the fabrication process. The printed beta-TC6 cells demonstrated continuous insulin secretion over a 6day period, which suggests that the printed cells are able to maintain normal cellular function within the microparticles. We show that the printing conditions, such as cell number, alginate concentration, and ionic strengths of CaCl2, influence cellular distribution and geometry of the printed particles. This study presents a simple and high-throughput method to encapsulate single cells, and this technique may be applied in various research investigations, including single-cell analysis, high-throughput drug screening, and stem cell differentiation at the single-cell level.

scholarly journals High-throughput label-free molecular fingerprinting flow cytometry

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0241 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Takuro Ideguchi ◽  
Yusuke Yonamine ◽  
SangWook Lee ◽  
Yizhi Luo ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Fluorescent Labeling ◽  
Live Cells ◽  
Molecular Fingerprinting ◽  
Label Free ◽  
Indispensable Tool

Flow cytometry is an indispensable tool in biology for counting and analyzing single cells in large heterogeneous populations. However, it predominantly relies on fluorescent labeling to differentiate cells and, hence, comes with several fundamental drawbacks. Here, we present a high-throughput Raman flow cytometer on a microfluidic chip that chemically probes single live cells in a label-free manner. It is based on a rapid-scan Fourier-transform coherent anti-Stokes Raman scattering spectrometer as an optical interrogator, enabling us to obtain the broadband molecular vibrational spectrum of every single cell in the fingerprint region (400 to 1600 cm−1) with a record-high throughput of ~2000 events/s. As a practical application of the method not feasible with conventional flow cytometry, we demonstrate high-throughput label-free single-cell analysis of the astaxanthin productivity and photosynthetic dynamics ofHaematococcus lacustris.

scholarly journals Quantification of single-cell nanoparticle concentrations and the distribution of these concentrations in cell population

2014 ◽  
Vol 11 (94) ◽  
pp. 20131152 ◽  
Author(s):  
Jason T. Rashkow ◽  
Sunny C. Patel ◽  
Ryan Tappero ◽  
Balaji Sitharaman
Keyword(s):  
Single Cell ◽  
Inductively Coupled Plasma ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Large Population ◽  
Cellular Heterogeneity ◽  
Physical Parameters ◽  
Animal Cells ◽  
Promising Tool ◽  
Icp Ms

Quantification of nanoparticle uptake into cells is necessary for numerous applications in cellular imaging and therapy. Herein, synchrotron X-ray fluorescence (SXRF) microscopy, a promising tool to quantify elements in plant and animal cells, was employed to quantify and characterize the distribution of titanium dioxide (TiO 2 ) nanosphere uptake in a population of single cells. These results were compared with average nanoparticle concentrations per cell obtained by widely used inductively coupled plasma mass spectrometry (ICP-MS). The results show that nanoparticle concentrations per cell quantified by SXRF were of one to two orders of magnitude greater compared with ICP-MS. The SXRF results also indicate a Gaussian distribution of the nanoparticle concentration per cell. The results suggest that issues relevant to the field of single-cell analysis, the limitation of methods to determine physical parameters from large population averages leading to potentially misleading information and the lack of any information about the cellular heterogeneity are equally relevant for quantification of nanoparticles in cell populations.

scholarly journals Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Harrison Specht ◽  
Edward Emmott ◽  
Aleksandra A. Petelski ◽  
R. Gray Huffman ◽  
David H. Perlman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Single Cell Protein ◽  
Cellular Heterogeneity ◽  
Cell Protein ◽  
Alternatively Activated Macrophages ◽  
Monocytes And Macrophages ◽  
Molecular Phenotypes ◽  
Post Transcriptional Regulation

Abstract Background Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. Results To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Conclusions Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

scholarly journals Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains

2018 ◽  
Author(s):  
Min Jung ◽  
Daniel Wells ◽  
Jannette Rusch ◽  
Suhaira Ahmed ◽  
Jonathan Marchini ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Web Application ◽  
Single Cell Analysis ◽  
De Novo ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Mouse Strains ◽  
Seminiferous Tubules ◽  
Sequence Motifs

AbstractBy removing the confounding factor of cellular heterogeneity, single cell genomics can revolutionize the study of development and disease, but methods are needed to simplify comparison among individuals. To develop such a framework, we assayed the transcriptome in 62,600 single cells from the testes of wildtype mice, and mice with gonadal defects due to disruption of the genes Mlh3, Hormad1, Cul4a or Cnp. The resulting expression atlas of distinct cell clusters revealed novel markers and new insights into testis gene regulation. By jointly analysing mutant and wildtype cells using a model-based factor analysis method, SDA, we decomposed our data into 46 components that identify novel meiotic gene regulatory programmes, mutant-specific pathological processes, and technical effects. Moreover, we identify, de novo, DNA sequence motifs associated with each component, and show that SDA can be used to impute expression values from single cell data. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/- and Hormad1-/- testes, an area typically associated with immune privilege. We provide a web application to enable interactive exploration of testis gene expression and components at http://www.stats.ox.ac.uk/~wells/testisAtlas.html

scholarly journals SIMBA: SIngle-cell eMBedding Along with features

2021 ◽  
Author(s):  
Huidong Chen ◽  
Jayoung Ryu ◽  
Michael Edward Vinyard ◽  
Adam Lerer ◽  
Luca Pinello
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Dna Sequences ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Omics Data ◽  
Cell Analysis ◽  
Genomic Features ◽  
The Individual

Recent advances in single cell omics technologies enable the individual or joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. However, cellular processes are defined in individual cells and inherently involve multiple genomic features that interact with each other and together provide complementary views on principles of gene regulation. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a single-cell embedding method that embeds single cells along with their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for cellular heterogeneity study, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a common way and thus simplifies the development of new analyses and integration of other single-cell modalities.

scholarly journals CoTECH for single-cell joint detection of transcriptome and chromatin occupancy

2021 ◽  
Author(s):  
Yingjie Luo ◽  
Haiqing Xiong ◽  
Qianhao Wang ◽  
Xianhong Yu ◽  
Aibin He
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Joint Detection ◽  
Biological Processes ◽  
Experimental Procedure ◽  
Higher Dimensional

Abstract Here we present CoTECH, a high-throughput co-aasay that measures chromatin occupancy and transcriptome in single cells. The CoTECH method adopts a combinatorial indexing strategy to enrich chromatin fragments of interest as reported in CoBATCH in combination with a modified Smart-seq2 procedure to simultaneously capture the 3’ mRNA profiles in the same single cells. The whole experimental procedure can be handled within three days.The CoTECH acquires data quality of 1000-9000 unique mapped reads (DNA partition) and 1500-4000 expressed genes (RNA partition) per cell. Experimentally linking chromatin occupancy to transcriptional outputs and inferred molecular association between multimodal omics datasets made possible by CoTECH enables reconstructions of higher dimensional epigenomic landscape, providing new insights into epigenome-centric gene regulation and cellular heterogeneity in many biological processes. This step-by-step protocol is related to the publication “Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions” in Nature Methods.

scholarly journals Fast and robust non-negative matrix factorization for single-cell experiments

2021 ◽  
Author(s):  
Zachary J. DeBruine ◽  
Karsten Melcher ◽  
Timothy J. Triche
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
R Package ◽  
Complex Data ◽  
Technological Advances ◽  
Value Decomposition ◽  
Non Negative Matrix Factorization

AbstractNon-negative matrix factorization (NMF) is an intuitively appealing method to extract additive combinations of measurements from noisy or complex data. NMF is applied broadly to text and image processing, time-series analysis, and genomics, where recent technological advances permit sequencing experiments to measure the representation of tens of thousands of features in millions of single cells. In these experiments, a count of zero for a given feature in a given cell may indicate either the absence of that feature or an insufficient read coverage to detect that feature (“dropout”). In contrast to spectral decompositions such as the Singular Value Decomposition (SVD), the strictly positive imputation of signal by NMF is an ideal fit for single-cell data with ambiguous zeroes. Nevertheless, most single-cell analysis pipelines apply SVD or Principal Component Analysis (PCA) on transformed counts because these implementations are fast while current NMF implementations are slow. To address this need, we present an accessible NMF implementation that is much faster than PCA and rivals the runtimes of state-of-the-art SVD. NMF models learned with our implementation from raw count matrices yield intuitive summaries of complex biological processes, capturing coordinated gene activity and enrichment of sample metadata. Our NMF implementation, available in the RcppML (Rcpp Machine Learning library) R package, improves upon current NMF implementations by introducing a scaling diagonal to enable convex L1 regularization for feature engineering, reproducible factor scalings, and symmetric factorizations. RcppML NMF easily handles sparse datasets with millions of samples, making NMF an attractive replacement for PCA in the analysis of single-cell experiments.

scholarly journals RefCell: Multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’

2018 ◽  
Author(s):  
Yang Shen ◽  
Nard Kubben ◽  
Julián Candia ◽  
Alexandre V. Morozov ◽  
Tom Misteli ◽  
...  
Keyword(s):  
Dimensional Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Premature Aging ◽  
Cellular Heterogeneity ◽  
High Level ◽  
High Throughput Screens ◽  
High Throughput Imaging

AbstractBackgroundImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Cutting-edge high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but they suffer from the “curse of dimensionality” and non-standardized outputs.ResultsHere we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states, and uses these “typical cells” as a reference for classification and weighting of metrics. RefCell quantitatively assesses the heterogeneous deviations from typical behavior for each analyzed perturbation or sample.ConclusionsWe apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin protein targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering based single cell analysis method, which both reveal more potential hits than conventional average based analysis.

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