scholarly journals Ultra-multiplexed analysis of single-cell dynamics reveals logic rules in differentiation

2019 ◽  
Vol 5 (4) ◽  
pp. eaav7959 ◽  
Author(s):  
Ce Zhang ◽  
Hsiung-Lin Tu ◽  
Gengjie Jia ◽  
Tanzila Mukhtar ◽  
Verdon Taylor ◽  
...  

Dynamical control of cellular microenvironments is highly desirable to study complex processes such as stem cell differentiation and immune signaling. We present an ultra-multiplexed microfluidic system for high-throughput single-cell analysis in precisely defined dynamic signaling environments. Our system delivers combinatorial and time-varying signals to 1500 independently programmable culture chambers in week-long live-cell experiments by performing nearly 106 pipetting steps, where single cells, two-dimensional (2D) populations, or 3D neurospheres are chemically stimulated and tracked. Using our system and statistical analysis, we investigated the signaling landscape of neural stem cell differentiation and discovered “cellular logic rules” that revealed the critical role of signal timing and sequence in cell fate decisions. We find synergistic and antagonistic signal interactions and show that differentiation pathways are highly redundant. Our system allows dissection of hidden aspects of cellular dynamics and enables accelerated biological discovery.

Author(s):  
Harrison Specht ◽  
Nikolai Slavov

Many pressing medical challenges - such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers - have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.


F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1158 ◽  
Author(s):  
Fanny Perraudeau ◽  
Davide Risso ◽  
Kelly Street ◽  
Elizabeth Purdom ◽  
Sandrine Dudoit

Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5598-5598
Author(s):  
Wen Zhou ◽  
Fenghuang Zhan

Abstract Single-cell analysis reveals sequential genetic events from hematopoietic stem cell differentiation hierarchy in multiple myeloma patients with FGFR3/IGH translocation Jiaojiao Guo1*, Yv Wang2*, Dehui Zou2*, Zhenhao Liu1, Dan Wu2, Lu Xie3, Fenghuang Zhan 4 , Guoji Guo5,Bing Liu6, Lugui Qiu2, Jiaxi Zhou2, Tao Cheng2#, Wen Zhou1# 1Cancer Research Institute,Central South University, Changsha 410078, China.2State Key Laboratory of Experimental Hematology, Chinese Academy of Medical Science,Tianjin, China.3Shanghai Center for Bioinformation Technology, Shanghai , China.4Department of Internal Medicine, Division of Hematology, University of Iowa, Iowa City, IA 52242, USA. 5Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang , China.6307-Ivy Translational Medicine Center, Laboratory of Oncology, Academy of Military Medical Sciences, Beijing , China. E-mail: [email protected]; [email protected] Background: It is generally believed that multiple myeloma (MM) cells develop from either B cells or plasma cells in the bone marrow (BM). However, the nature of the earliest precursors of MM cells is still poorly defined and existing data are conflicting regarding the origin of MM cells. How these genes cooperate with other MM-related factors to induce MM oncogenic transformation in the hematopoietic hierarchy needs to be further defined. Materials and Methods: In this study, we employed single-cell gene expression analysis of HSC-PC lineage including HSC, MLP, B cells, Plasmablasts (PBs) and Plasma cells (PC) from one health donor (HD), one paired monozygotic twin(one is HD and another one suffered with MM), MM patients at diagnosis (AD) and relapse (RE) with FGFR3/IGH translocation. 672 individual cells from the 8 population were sorted for single cell real-time PCR reaction. Results: We focused on the HSC-PC lineage because of its implication in MM and identified several differentially expressed genes (DEGs) in the HSC compartment with continuous activation throughout HSC-PCs. Among those, BACH2FA and DDIT3 showed consistent up-regulation, while FOXO3 and IL1 were down-regulated continuously. 7 DEGs (SIPA1L2, RBM34, SPI1, MDM2, AKAP12, DKK1 and c-Myc) showed overlap among the three conditions (HD, AD and RE, respectively). Kaplan-Meier analysis revealed HSC-associated signatures (HAS) in overlapping and non-overlapping DEGs with poor survival outcome, p=0.000). To compare genetic alterations between sporadic and hereditary MM patients, we analyzed two groups of DEGs - one from sporadic MM patients (AD) and HD; the other from the hereditary MM patient (AD) and the healthy counterpart of its paired monozygotic twin- along the HSC differentiation hierarchy. We identified both overlapping and non-overlapping DEGs. For example, MDM2 and c-Myc were over-expressed at the HSC and the MLP stage, respectively, in both groups. However, ARUKA was not activated in the monozygotic twin group, presumably because of the lack of FGFR3 translocation. By using fluorescence in situ hybridization (FISH) analysis, we detected IGH and FGFR3 translocation in 50-60% and 11% MM patients, respectively. These chromosomal rearrangements have been used to identify tumor plasma cells. FGFR3-translocated was found in 18%, 23% and 44% for B cells, PBs and plasma cells, respectively. Our work extends previous discoveries of chromosome translocation in malignant plasma cells, suggesting existence of heterogeneity in cells with FGFR3 translocation. Furthermore, FGFR3 translocation was accompanied by alteration of ARUKA. While B cell differentiation associated genes begin to express in the plasmablast stage, expression of multi-drug resistance genes occurs only in plasma cells in relapsed patients. Together, these molecular alterations likely constitute key elements of a sophisticated genetic program underlying the occurrence of MM. Conclusion: Our findings reveal the existence of genetically distinct cellular origins of MM cells from the HSC differentiation hierarchy within each MM tumor mass. FGFR3 translocation was accompanied by alteration of ARUKA, indicating FGFR3 translocation might cause CIN gene activation. Finally, we find that a small percentage of HSCs from MM patients could harbor oncogenic activation, thereby pointing to the need of a more vigorous quality control in autologous HSC transplantation for MM patients. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Vol 34 (30) ◽  
pp. 2050288
Author(s):  
Y. Ye ◽  
Z. Yang ◽  
M. Zhu ◽  
J. Lei

Induced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.


2018 ◽  
Author(s):  
Harrison Specht ◽  
Nikolai Slavov

Many pressing medical challenges - such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers - have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.


2020 ◽  
Author(s):  
Koon-Kiu Yan ◽  
Erin Nekritz ◽  
Bensheng Ju ◽  
Xinran Dong ◽  
Rachel Werner ◽  
...  

ABSTRACTIt is well known that the expansion of the mammary epithelium during the ovarian cycles in female mammals is supported by the transient increase in mammary epithelial stem cells (MaSCs). However, dissecting the molecular mechanisms that govern MaSC function and differentiation is poorly understood due to the lack of standardized methods for their identification and isolation.The development of robust single-cell mRNA sequencing () technologies and the computational methods to analyze them provides us with novel tools to approach the challenge of studying MaSCs in a completely unbiased way without. Here, we have performed the largest scRNA-seq analysis of individual mammary epithelial cells (~70,000 cells). Our study identified a distinct cell population presenting molecular features of MaSCs.Importantly, further purification and additional in-depth single-cell analysis of these cells revealed that they are not a fully homogenous entity. Instead, we identified three subpopulations representing early stages of lineage commitment. By tracking their molecular evolution through single-cell network analysis we found that one of these subpopulations represents bipotent MaSCs from which luminal and basal lineages diverge. Importantly, we also confirmed the presence of these cells in human mammary glands. Finally, through expression and network analysis studies, we have uncovered transcription factors that are activated early during lineage commitment. These data identified E2-2 (Tcf4) and ID3 as a potential molecular switch of mammary epithelial stem cell differentiation.


2017 ◽  
Author(s):  
Idse Heemskerk ◽  
Kari Burt ◽  
Matthew Miller ◽  
Sapna Chhabra ◽  
M. Cecilia Guerra ◽  
...  

During embryonic development, diffusible signaling molecules called morphogens are thought to determine cell fates in a concentration-dependent manner1–4, and protocols for directed stem cell differentiation are based on this picture5–8. However, in the mammalian embryo, morphogen concentrations change rapidly compared to the time for making cell fate decisions9–12. It is unknown how changing ligand levels are interpreted, and whether the precise timecourse of ligand exposure plays a role in cell fate decisions. Nodal and BMP4 are morphogens crucial for gastrulation in vertebrates13. Each pathway has distinct receptor complexes that phosphorylate specific signal transducers, known as receptor-Smads, which then complex with the shared cofactor Smad4 to activate target genes14. Here we show in human embryonic stem cells (hESCs) that the response to BMP4 signaling indeed is determined by the ligand concentration, but that unexpectedly, the expression of many mesodermal targets of Activin/Nodal depends on rate of concentration increase. In addition, we use live imaging of hESCs with GFP integrated at the endogenous SMAD4 locus to show that a stem cell model for the human embryo15 generates a wave of Nodal signaling. Cells experience rapidly increasing Nodal specifically in the region of mesendoderm differentiation. We also demonstrate that pulsatile stimulation with Activin induces repeated strong signaling and enhances mesoderm differentiation. Our results break with the paradigm of concentration-dependent differentiation and demonstrate an important role for morphogen dynamics in the cell fate decisions associated with mammalian gastrulation. They suggest a highly dynamic picture of embryonic patterning where some cell fates depend on rapid concentration increase rather than absolute levels, and point to ligand dynamics as a new dimension to optimize protocols for directed stem cell differentiation.


Author(s):  
Yusong Ye ◽  
Zhuoqin Yang ◽  
Meixia Zhu ◽  
Jinzhi Lei

AbstractInduced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.


2018 ◽  
Author(s):  
Harrison Specht ◽  
Nikolai Slavov

Many pressing medical challenges -- such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers -- have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.


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