scholarly journals Mapping human cell phenotypes to genotypes with single-cell genomics

Science ◽  
2019 ◽  
Vol 365 (6460) ◽  
pp. 1401-1405 ◽  
Author(s):  
J. Gray Camp ◽  
Randall Platt ◽  
Barbara Treutlein
Keyword(s):  
Single Cell ◽  
Human Cell ◽  
Cell Biology ◽  
Life Histories ◽  
Gene Editing ◽  
Cell Types ◽  
Single Cell Genomics ◽  
Cumulative Activity ◽  
Cellular Phenotypes ◽  
Cell Phenotypes

The cumulative activity of all of the body’s cells, with their myriad interactions, life histories, and environmental experiences, gives rise to a condition that is distinctly human and specific to each individual. It is an enduring goal to catalog our human cell types, to understand how they develop, how they vary between individuals, and how they fail in disease. Single-cell genomics has revolutionized this endeavor because sequencing-based methods provide a means to quantitatively annotate cell states on the basis of high-information content and high-throughput measurements. Together with advances in stem cell biology and gene editing, we are in the midst of a fascinating journey to understand the cellular phenotypes that compose human bodies and how the human genome is used to build and maintain each cell. Here, we will review recent advances into how single-cell genomics is being used to develop personalized phenotyping strategies that cross subcellular, cellular, and tissue scales to link our genome to our cumulative cellular phenotypes.

scholarly journals An era of single-cell genomics consortia

2020 ◽  
Vol 52 (9) ◽  
pp. 1409-1418 ◽  
Author(s):  
Yoshinari Ando ◽  
Andrew Tae-Jun Kwon ◽  
Jay W. Shin
Keyword(s):  
Single Cell ◽  
Human Body ◽  
Human Cell ◽  
Single Cells ◽  
Cell Types ◽  
Regional Environment ◽  
Single Cell Genomics ◽  
Genomic Technologies ◽  
Data Collection And Analysis ◽  
Wide Range

Abstract The human body consists of 37 trillion single cells represented by over 50 organs that are stitched together to make us who we are, yet we still have very little understanding about the basic units of our body: what cell types and states make up our organs both compositionally and spatially. Previous efforts to profile a wide range of human cell types have been attempted by the FANTOM and GTEx consortia. Now, with the advancement in genomic technologies, profiling the human body at single-cell resolution is possible and will generate an unprecedented wealth of data that will accelerate basic and clinical research with tangible applications to future medicine. To date, several major organs have been profiled, but the challenges lie in ways to integrate single-cell genomics data in a meaningful way. In recent years, several consortia have begun to introduce harmonization and equity in data collection and analysis. Herein, we introduce existing and nascent single-cell genomics consortia, and present benefits to necessitate single-cell genomic consortia in a regional environment to achieve the universal human cell reference dataset.

scholarly journals Single-cell classification using learned cell phenotypes

2020 ◽  
Author(s):  
Yang Chen ◽  
Tadepally Lakshmikanth ◽  
Jaromir Mikes ◽  
Petter Brodin
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
High Dimensional Data ◽  
Cell Types ◽  
High Dimensional ◽  
Manual Annotation ◽  
Cell Classification ◽  
Manual Inspection ◽  
Manual Sorting ◽  
Cell Phenotypes

AbstractSingle-cell methods such as flow cytometry, Mass cytometry and single-cell mRNA sequencing collect high-dimensional data on thousands to millions of individual cells. An important aim during the analysis of such data is to classify cells into known categories and cell types. One commonly used approach towards this is clustering of cells with similar features followed by manual annotation of clusters in relation to known biology. A second approach, commonly used for cytometry data relies on manual sorting or “gating” of cells, often based on pairwise combinations of measurements used in a stepwise and very tedious process of cell annotation. Both of these approaches require manual inspection and annotation of every new dataset generated, a process that is not only time consuming but also subjective and surely influential for the conclusions drawn. The manual annotation is also difficult to reproduce by other researchers with a different perception of features that signify their cells of interest. Here we propose an alternative strategy based on machine learning of known phenotypes from manually curated, high-dimensional data and thereby enabling rapid classification of subsequent datasets in a more reproducible manner. This simple approach increases both throughput, reproducibility and simplicity of cell classification in single-cell biology.

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

Single-cell genomics and gene editing: implications for nephrology

2018 ◽  
Vol 15 (2) ◽  
pp. 63-64 ◽  
Author(s):  
Parker C. Wilson ◽  
Benjamin D. Humphreys
Keyword(s):  
Single Cell ◽  
Gene Editing ◽  
Single Cell Genomics

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

scholarly journals Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Biomedical Research ◽  
Human Cell ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Cellular phenotypes of normal and leukemic hemopoietic cells determined by analysis with selected antibody combinations

Blood ◽  
1980 ◽  
Vol 56 (3) ◽  
pp. 430-441 ◽  
Author(s):  
G Janossy ◽  
FJ Bollum ◽  
KF Bradstock ◽  
J Ashley
Keyword(s):  
Bone Marrow ◽  
Monoclonal Antibodies ◽  
Single Cell ◽  
Normal Cell ◽  
Cell Types ◽  
Precursor Cells ◽  
Leukemic Cells ◽  
Double Staining ◽  
Cell Assays ◽  
Cellular Phenotypes

Abstract Individual leukemic cells and the corresponding rare normal cell types in nonleukemic bone marrow were analyzed with various combinations of antisera (labeled with different fluorochromes: TRITC and FITC). Double staining for membrane Ia-like molecules (TRITC) and nuclear terminal transferase (FITC) was a very useful combination that distinguished common non-T, non-B ALL (Ia+,TdT+) and thymic ALL (Ia-,TdT+) from the rare cases of B ALL (Ia+,TdT-) and from AML (frequently Ia+, TdT-; in some cases Ia-, TdT-). Additional antisera (such as anti-ALL, anti- HuTLA, anti-immunoglobulin reagents, etc.) confirmed the diagnosis and further characterized the leukemic blasts. Ia+,TdT+ cells could be observed in low numbers in normal and nonleukemic regenerating marrow and were probably normal precursor cells; this reagent combinations was, therefore, not useful for monitoring residual non-T, non-B ALL blasts in treated patients. Other marker combinations detecting pre-B ALL blasts (double staining for cytoplasmic IgM and nuclear TdT) and Thy-ALL blasts (HuTLA+,TdT+) were, however, virtually leukemia specific in the bone marrow and could be used to effectively monitor residual leukemic cells throughout the disease. These combined single-cell assays are not only economical and informative but are also important for assessing the heterogeneity of leukemia and for standardizing new mouse or rat monoclonal antibodies for diagnosis.

scholarly journals Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

2020 ◽  
Author(s):  
Shuai He ◽  
Lin-He Wang ◽  
Yang Liu ◽  
Yi-Qi Li ◽  
Hai-Tian Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cell ◽  
Regulatory Networks ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Cell Receptor ◽  
Marker Genes ◽  
Human Organs ◽  
Adult Human ◽  
Receptor Repertoire

ABSTRACTBackgroundAs core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing.ResultsWe perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues.ConclusionsThe adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

scholarly journals A case study on the detailed reproducibility of a human cell atlas project

2018 ◽  
Author(s):  
Kui Hua ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Human Cell ◽  
Scientific Discovery ◽  
Cell Types ◽  
The Future ◽  
Reproduction Study ◽  
High Flexibility ◽  
Cell Data ◽  
Different Levels

AbstractReproducibility is a defining feature of a scientific discovery. Reproducibility can be at different levels for different types of study. The purpose of the Human Cell Atlas (HCA) project is to build maps of molecular signatures of all human cell types and states to serve as references for future discoveries. Constructing such a complex reference atlas must involve the assembly and aggregation of data from multiple labs, probably generated with different technologies. It has much higher requirements on reproducibility than individual research projects. To add another layer of complexity, the bioinformatics procedures involved for single-cell data have high flexibility and diversity. There are many factors in the processing and analysis of single-cell RNA-seq data that can shape the final results in different ways. To study what levels of reproducibility can be reached in current practices, we conducted a detailed reproduction study for a well-documented recent publication on the atlas of human blood dendritic cells as an example to break down the bioinformatics steps and factors that are crucial for the reproducibility at different levels. We found that the major scientific discovery can be well reproduced after some efforts, but there are also some differences in some details that may cause uncertainty in the future reference. This study provides a detailed case observation on the on-going discussions of the type of standards the HCA community should take when releasing data and publications to guarantee the reproducibility and reliability of the future atlas.

scholarly journals Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

2021 ◽  
Vol 12 ◽  
Author(s):  
John W. Hickey ◽  
Yuqi Tan ◽  
Garry P. Nolan ◽  
Yury Goltsev
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Clustering Algorithm ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Cell Segmentation ◽  
Imaging Data ◽  
Cell Type ◽  
Multiplexed Imaging ◽  
Four Levels

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.

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