Improved SNV discovery in barcode-stratified scRNA-seq alignments

Single cell SNV analysis is an emerging and promising strategy to connect cell-level genetic variation to cell phenotypes. At the present, SNV detection from 10x Genomics scRNA-seq data is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gain of information of SNV assessments from individual cell scRNA-seq data, where the alignments are split by barcode prior to the variant call. For our analyses we use publicly available sequencing da-ta on the human breast cancer cell line MCF7 cell line generated at consequent time-points during anticancer treatment. We analysed SNV calls by three popular variant callers, GATK, Strelka2 and Mutect2, in combination with a method for cell-level tabulation of the sequencing read counts bearing SNV alleles, SCReadCounts. Our analysis shows that variant calls on individual cell alignments identify at least two-fold higher number of SNVs as compared to the pooled scRNA-seq. We demonstrate that scSNVs exclusively called in the single cell alignments (scSNVs) are substantially enriched in novel genetic variants and in coding functional annotations, in particular, stop-codon and missense substitutions. Furthermore, we find that the expression of some scSNVs correlates with the expression of their harbouring gene (cis-scReQTLs). Overall, our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes on the need of cell-level variant detection approaches and tools. Given the growing accumulation of scRNA-seq datasets, cell-level variant assessments are likely to significantly contribute to the understanding of the cellular heterogeneity and the relationship between genetics variants and functional phenotypes. In addition, cell-level variant assessments from scRNA-seq can be highly informative in cancer where they can help elucidate somatic mutations evolution and functionality.

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Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

Genes ◽

10.3390/genes12101558 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1558

Author(s):

Prashant N. M. ◽

Hongyu Liu ◽

Christian Dillard ◽

Helen Ibeawuchi ◽

Turkey Alsaeedy ◽

...

Keyword(s):

Single Cell ◽

Individual Cell ◽

Stop Codon ◽

Cellular Heterogeneity ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Cell Level ◽

Novel Variants ◽

Anticancer Treatment ◽

Mcf7 Cell Line

Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v2 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D. Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.1101/2021.08.19.457000 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v1 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

T. Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Extracting cancer cell line electrochemical parameters at the single cell level using a microfabricated device

Biotechnology Journal ◽

10.1002/biot.200800321 ◽

2009 ◽

Vol 4 (2) ◽

pp. 216-223 ◽

Cited By ~ 4

Author(s):

Jassim A. Alqabandi ◽

Ussama M. Abdel-Motal ◽

Kamal Youcef-Toumi

Keyword(s):

Single Cell ◽

Cell Line ◽

Cancer Cell ◽

Cancer Cell Line ◽

Single Cell Level ◽

Cell Level ◽

Electrochemical Parameters

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An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

10.2196/preprints.13932 ◽

2019 ◽

Author(s):

Ruixin Wang ◽

Dongni Wang ◽

Dekai Kang ◽

Xusen Guo ◽

Chong Guo ◽

...

Keyword(s):

Single Cell ◽

Cell Line ◽

Cell Lines ◽

Cell Morphology ◽

Single Cell Level ◽

Cross Contamination ◽

Cell Level ◽

Cell Identification ◽

Pure Cell ◽

Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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Microfluidic Analyzer Enabling Quantitative Measurements of Specific Intracellular Proteins at the Single-Cell Level

Micromachines ◽

10.3390/mi9110588 ◽

2018 ◽

Vol 9 (11) ◽

pp. 588 ◽

Cited By ~ 2

Author(s):

Lixing Liu ◽

Beiyuan Fan ◽

Diancan Wang ◽

Xiufeng Li ◽

Yeqing Song ◽

...

Keyword(s):

Adenoid Cystic Carcinoma ◽

Single Cell ◽

Cell Line ◽

Oral Carcinoma ◽

Parental Cell Line ◽

Single Cell Level ◽

Cell Level ◽

Salivary Adenoid Cystic Carcinoma ◽

Adenoid Cystic ◽

Intracellular Proteins

This paper presents a microfluidic instrument capable of quantifying single-cell specific intracellular proteins, which are composed of three functioning modules and two software platforms. Under the control of a LabVIEW platform, a pressure module flushed cells stained with fluorescent antibodies through a microfluidic module with fluorescent intensities quantified by a fluorescent module and translated into the numbers of specific intracellular proteins at the single-cell level using a MATLAB platform. Detection ranges and resolutions of the analyzer were characterized as 896.78–6.78 × 105 and 334.60 nM for Alexa 488, 314.60–2.11 × 105 and 153.98 nM for FITC, and 77.03–5.24 × 104 and 37.17 nM for FITC-labelled anti-beta-actin antibodies. As a demonstration, the numbers of single-cell beta-actins of two paired oral tumor cell types and two oral patient samples were quantified as: 1.12 ± 0.77 × 106/cell (salivary adenoid cystic carcinoma parental cell line (SACC-83), ncell = 13,689) vs. 0.90 ± 0.58 × 105/cell (salivary adenoid cystic carcinoma lung metastasis cell line (SACC-LM), ncell = 15,341); 0.89 ± 0.69 × 106/cell (oral carcinoma cell line (CAL 27), ncell = 7357) vs. 0.93 ± 0.69 × 106/cell (oral carcinoma lymphatic metastasis cell line (CAL 27-LN2), ncell = 6276); and 0.86 ± 0.52 × 106/cell (patient I) vs. 0.85 ± 0.58 × 106/cell (patient II). These results (1) validated the developed analyzer with a throughput of 10 cells/s and a processing capability of ~10,000 cells for each cell type, and (2) revealed that as an internal control in cell analysis, the expressions of beta-actins remained stable in oral tumors with different malignant levels.

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scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz701 ◽

2019 ◽

Cited By ~ 2

Author(s):

Congting Ye ◽

Qian Zhou ◽

Xiaohui Wu ◽

Chen Yu ◽

Guoli Ji ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Rna Seq ◽

Computational Tool ◽

Cell Level ◽

Wilcoxon Rank Sum Test ◽

Transcriptional Regulatory ◽

Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Novel 1,8-Naphthyridine Derivatives: Design, Synthesis and in vitro screening of their cytotoxic activity against MCF7 cell line

Open Chemistry ◽

10.1515/chem-2019-0097 ◽

2019 ◽

Vol 17 (1) ◽

pp. 943-954

Author(s):

Abeer N. Al-romaizan ◽

Thoraya S. Jaber ◽

Nesreen S. Ahmed

Keyword(s):

Cell Line ◽

Human Breast Cancer ◽

Mcf7 Cell ◽

Cancer Cell Line ◽

Human Breast Cancer Cell ◽

The Other ◽

Human Breast ◽

Design Synthesis ◽

Mcf7 Cell Line

AbstractA series of new 2-phenyl-7-methyl-1,8-naphthyridine derivatives with variable substituents at C3 were synthesized for an in vitro evaluation of their anticancer activity against human breast cancer cell line (MCF7). On one hand, compounds 3f, 6f, 8c, and 10b showed IC50 values (6.53, 7.88, 7.89, 7.79 μM, respectively) compared to that of the mentioned drug staurosparine (IC50 = 4.51 μM). On the other hand, derivatives 10c, 8d, 4d, 10f and 8b displayed better activity than staurosporin with IC50 values (1.47, 1.62, 1.68, 2.30, 3.19 μM, respectively).

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