Single-cell Transcription Research Articles

High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics.

Mining the antibody repertoire of plasma cells and plasmablasts could enable the discovery of useful antibodies for therapeutic or research purposes1. We present a method for high-throughput, single-cell screening of IgG-secreting primary cells to characterize antibody binding to soluble and membrane-bound antigens. CelliGO is a droplet microfluidics system that combines high-throughput screening for IgG activity, using fluorescence-based in-droplet single-cell bioassays2, with sequencing of paired antibody V genes, using in-droplet single-cell barcoded reverse transcription. We analyzed IgG repertoire diversity, clonal expansion and somatic hypermutation in cells from mice immunized with a vaccine target, a multifunctional enzyme or a membrane-bound cancer target. Immunization with these antigens yielded 100-1,000 IgG sequences per mouse. We generated 77 recombinant antibodies from the identified sequences and found that 93% recognized the soluble antigen and 14% the membrane antigen. The platform also allowed recovery of ~450-900 IgG sequences from ~2,200 IgG-secreting activated human memory B cells, activated ex vivo, demonstrating its versatility.

Projected t-SNE for batch correction.

Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data. The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours. Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies. aliverti@stat.unipd.it.

Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies.

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates using relatively homogeneous cell populations. However, to determine these rates during complex dynamical processes, for instance during cellular differentiation, single-cell resolution is required. Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory strategies. These strategies have distinct consequences for controlling the dynamic range and precision of gene expression. These findings advance our understanding of how individual cells in heterogeneous populations shape their gene expression dynamics.

Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M

Simultaneous observation of 3D chromatin organization and transcription at the single-cell level and with high spatial resolution may hold the key to unveiling the mechanisms regulating embryonic development, cell differentiation and even disease. We recently developed Hi-M, a technology that enables the sequential labeling, 3D imaging and localization of multiple genomic DNA loci, together with RNA expression, in single cells within whole, intact Drosophila embryos. Importantly, Hi-M enables simultaneous detection of RNA expression and chromosome organization without requiring sample unmounting and primary probe rehybridization. Here, we provide a step-by-step protocol describing the design of probes, the preparation of samples, the stable immobilization of embryos in microfluidic chambers, and the complete procedure for image acquisition. The combined RNA/DNA fluorescence in situ hybridization procedure takes 4-5 d, including embryo collection. In addition, we describe image analysis software to segment nuclei, detect genomic spots, correct for drift and produce Hi-M matrices. A typical Hi-M experiment takes 1-2 d to complete all rounds of labeling and imaging and 4 additional days for image analysis. This technology can be easily expanded to investigate cell differentiation in cultured cells or organization of chromatin within complex tissues.

Localization of Cell Receptor-Related Genes of SARS-CoV-2 in the Kidney through Single-Cell Transcriptome Analysis

Background: The new coronavirus (SARS-CoV-2), which has been responsible for the recent coronavirus disease 2019 (COVID-19) pandemic, uses the cell receptor angiotensin converting enzyme-2 (ACE2) for entry and the serine protease TMPRSS2 for spike (S) protein priming. Meanwhile, the presence of B⁰AT1 (SLC6A19) may prevent TMPRSS2 from accessing the cutting position on ACE2. Identifying the expression of these cell receptor-related genes of SARS-CoV-2 is critical for understanding the pathogenesis of SARS-CoV-2 in various tissues, especially in the kidney. Methods: The single-cell transcription datasets of the human cell landscape (HCL) and other relevant single-cell transcription databases were used to analyze the expression of ACE2, TMPRSS2, and SLC6A19 in various organs and tissues, but mainly in the kidney. Results: ACE2 was significantly expressed in the S1, S2, and S3 segments of proximal tubule (PT) cells. TMPRSS2 was widely expressed in several renal tubule populations extending from the PT cells to the collection system cell type, of which intercalated cells and the distal convoluted tubule cells showed more significant expression than PT cells. Unexpectedly, although expressed on various renal tubule populations, SLC6A19 was mainly enriched in PT cells, in line with ACE2 expression. Although alveolar-type (AT) 2 cells of the lung and intestinal epithelial cells expressed ACE2 as well as PT cells, AT 2 cells significantly expressed TMPRSS2 but not SLC6A19, while all 3 genes were significantly expressed in intestinal epithelial cells. Conclusions: ACE2 was widely expressed in specific cell subgroups of various human tissues, especially in intestinal epithelial cells, kidney PT cells, and also AT 2 cells of the lung. These 3 types of cells demonstrated significant differences in the distribution of the cell receptor-related genes of SARS-CoV-2, which may indicate the diversity of cell surface structures and differences in the affinity between SARS-CoV-2 and cells.

Excessive E2F Transcription in Single Cancer Cells Precludes Transient Cell Cycle Exit after DNA Damage

Excessive E2F Transcription in Single Cancer Cells Precludes Transient Cell Cycle Exit after DNA Damage

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs.

In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data.

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

An Inhibitory Lateral Hypothalamic-Preoptic Circuit Mediates Rapid Arousals from Sleep.

Among the neuronal populations implicated in sleep-wake control, the ventrolateral preoptic (VLPO) nucleus has emerged as a key sleep-promoting center. However, the synaptic drives that regulate the VLPO to control arousal levels invivo have not to date been identified. Here, we show that sleep-promoting galaninergic neurons within the VLPO nucleus, defined pharmacologically and by single-cell transcript analysis, are postsynaptic targets of lateral hypothalamic GABAergic (LHGABA) neurons and that activation of this pathway invivo rapidly drives wakefulness. Ca2+ imaging from LHGABA neurons indicate that they are both wake and rapid eye movement (REM)-sleep active. Consistent with the potent arousal-promoting property of the LHGABA → VLPO pathway, presynaptic inputs to LHGABA neurons originate from several canonical stress- and arousal-related network nodes. This work represents the first demonstration that direct synaptic inhibition of the VLPO area can suppress sleep-promoting neurons to rapidly promote arousal.

Longitudinal Tracking of Acute Myeloid Leukemia Stem Cells in Xenografts and Patients By microRNA Reporters and Single Cell RNA Sequencing

Current understanding of acute myeloid leukemia (AML) assumes a developmental hierarchy, in which a minor fraction of primitive and quiescent leukemia stem cells (LSC) sustain clonal propagation of disease. These therapy-resistant LSC may be the basis of relapse, as supported by data correlating the presence of LSC gene expression signatures at diagnosis with poor prognosis (Ng et al, Nature 2016). Novel approaches tracing LSC fates at single cell level before, during and after chemotherapy (CTX) are needed to confirm their biological relevance and derive new, LSC-focused diagnostic and therapeutic strategies. We and others have previously linked key LSC properties, such as quiescence and therapy resistance, to complex transcriptional regulation orchestrated by miR-126 (Lechman et al, Cancer Cell 2016). Furthermore, we provided proof of concept that LSCs could be prospectively isolated as miR-126(high) cells exploiting a lentiviral reporter vector capturing miR-126 bioactivity in live cells with single cell resolution. To extend these studies, we transduced primary blasts from n=3 AML patients (pts) carrying NPM1 and FLT3-ITD mutations with the miR-126 reporter, followed by xenografting (PDX). Blasts showed intra-tumor heterogeneity (ITH) in terms of miR-126 activity, with a minor fraction identified as miR-126(high). Limiting dilution secondary transplantation of FACS sorted miR-126(high) and -(low) blasts proved strong enrichment of repopulating activity within the miR-126(high) compartment in all 3 pts. On the contrary, no LSC enrichment could be verified in the CD34+CD38- fraction in 1 patient, suggesting that high miR-126 activity represents a more robust LSC identifier than commonly used surface markers. Next, we investigated the impact of daunorubicin and cytarabine CTX on miR-126-reporter+ blasts (n=3 AML) in PDX. Surprisingly, overall miR-126 activity diminished in post CTX residual AML compared to controls, compatible with a loss of blast quiescence. CTX accentuated ITH by uncovering a subset of blasts with very high levels of miR-126, distinct from the bulk population, which may correspond to residual quiescent LSC (Fig A). We then performed bulk RNA sequencing on miR-126(high) and miR-126(low) subsets from CTX and control PDX. While miR-126(low) blasts from both groups expressed markers of myeloid differentiation, miR-126(high) blasts were enriched for published hematopoietic stem cell hallmark signatures. Integrating differentially expressed genes between miR-126(high) and-(low) subsets at steady state and post CTX, we extrapolated a novel 8 gene signature associated with miR-126(high) blasts. Of note, patients from the AML TCGA PanCancer Atlas Cohort (n=161) harboring overexpression in one or more of these 8 genes had significantly decreased overall survival (10 vs 19 months, Logrank test p-value = 0.018). To further test whether our 8-gene miR-126(high) signature reveals ITH in patients, we performed single cell RNA sequencing (scRNAseq) of AML patient BM aspirates at diagnosis (n= 6). Blasts were identified based on the detection of mutated NPM1 transcripts in single cells. In 5 out of 7 patients expression of the miR-126 signature mapped to specific clusters of blasts identified by unsupervised shared nearest neighbor algorithm, confirming that it identifies ITH in patient samples. Interestingly, we detected miR-126 signature(high) blasts in pts with poor prognosis (n=4) and not in those with favorable outcome (n=2) (Fig B). To investigate ITH across longitudinal samples, we next performed scRNAseq of residual AML from a representative patient assessed early after CTX. In line with our PDX CTX model displaying increased miR-126 ITH, blasts on day14 of induction CTX segregated into 2 different clusters: cluster 1 containing LSC-like cells with miR-126(high) signature and similar transcriptional profile to the diagnosis counterpart; cluster 2, instead, was composed of actively cycling blasts. Residual blasts at day30, in addition to uniformly expressing the miR-126(high) signature, differed from diagnosis and day14 blasts by displaying cell cycle quiescence and induction of oxidative phosphorylation genes (Fig C). In summary, we have set up and applied PDX LSC modeling to clinically relevant patient samples to address AML intra-tumor heterogeneity and to pinpoint novel relevant transcriptomic features of LSC at diagnosis and after chemotherapy. Figure Disclosures Gentner: Genenta Science: Consultancy, Equity Ownership, Research Funding.

Single-Cell RNA Sequencing of Healthy Human Marrow Hematopoietic Cells

Hematopoiesis, especially the early events of blood cell formation, has been mainly studied in bulk populations of cells and using progenitor colony formation assays; the familiar hierarchy of cell lineage differentiation and maturation, and associated regulatory factors have been inferred from these methods. However, these techniques often require extensive manipulation of cells, the exposure of cells to unphysiological conditions, aggregation of heterogeneous populations, and prior assumptions concerning cell function and gene expression. New single cell methodology avoids many of these potential experimental deficiencies. Here we have applied single-cell RNA-sequencing(scRNA-seq)to fresh human bone marrow CD34+cells: we profiled 391 single hematopoietic stem/progenitor cells (HSPCs) from four healthy donors by deep sequencing of individual cell transcriptomes. An average of 4560 protein-coding genes were detected per cell. Cells clustered into six distinct groups, which could be assigned to known HSPC subpopulations (Fig 1A), based on expression of lineage-specific genes. Lin-CD34+CD38+cells emerged as locally clustered cell populations (Clusters 2-6, including MEP, GMP, ETP and ProB), while Lin-CD34+CD38-cells formed a single cluster (HSC/MLP). Reconstruction of differentiation trajectories by transcription in single cells revealed four committed lineages derived from stem cell compartment. The earliest fate split separates MEPs from MLPs, which partition further into lymphoid, and granulocyte-monocyte progenitors (Fig 1B). The overall pattern differs from the classical hematopoietic model describing a single binary split between myeloid and lymphoid differentiation immediately downstream of multipotent cells. However, our data align well to recently published scRNA-seq data showing sequential commitment of stem cells to the lymphoid, erythroid/megakaryocytic, and finally myeloid lineages (Setty M, Nat Biotechnol2019; Pellin D, Nat Commun2019). We further examined trends in gene expression in each of the branches and found dynamic expression changes underlying cell fate during early lineage differentiation (Fig 1C). As confirmation, PCA plot of published single-cell assay for transposase-accessible chromatin (scATAC-seq) shows similar differentiation pattern. After projecting scATAC-seq data to our transcriptomic clusters' specific genes, MEP-dependent and myeloid/lymphoid-dependent genes were located on opposing sides of the PC1 with same direction (Fig 1D), indicating transcriptome and epigenome work on differentiation in concerted effort. scRNA-seq provides opportunities for discovery and characterization at the molecular levels of early HSC differentiation and developmental intermediates, retrospectively, without the need to isolate purified populations. However, information inferred from scRNA-seq may be obscured due to missing reads and limited cell numbers. More cells would provide greater detail and higher resolution mapping.Given the low frequency of megakaryocyte progenitors within the CD34+cells as well as the neglected Lin-CD34-BM compartment, we could not fully resolve the separation and maturation of all lineages. Nonetheless, we found good coverage of cell types and a similar HSPC Atlas as other published studies (Velten L, Nat Cell Biol2017; Pellin D, Nat Commun2019)despite our limited numbers of starting cells. Our data accurately reflect the pattern of normal hematopoiesis, which may help to revise and refine characterization of hematopoiesis and provide a general reference framework to investigate the complexities of blood cell production at single-cell resolution - especially when cell numbers are limited, as from patient samples and in marrow failure syndromes. Fig. 1scRNA-seq of human hematopoietic stem and progenitor cells. (A) Unsupervised hierarchical clustering of gene expression data for all cells. C1, HSC/MLP; C2, MEP; C3, GMP; C4, ProB; C5-C6, ETP. (B)Visualization of the HSPC continuum. Each ball represents one cell.(C) Large-scale shifts in gene expression during development of hematopoietic cells.Bars on top indicate locations of individual cells, colored by stages of development, along this developmental trajectory. (D) Projections of five transcriptomic gene modules onto PCA of scATAC-seq data (Buenrostro JD,Cell 2018). Each dot represents a transcriptional factor. Figure 1 Disclosures No relevant conflicts of interest to declare.

Comparative analysis of cellular expression pattern of schizophrenia risk genes in human versus mouse cortex

BackgroundSchizophrenia is a common psychiatric disease with high hereditary. The identification of schizophrenia risk genes (SRG) has shed light on its pathophysiological mechanisms. Mouse genetic models have been widely used to study the function of SRG in the brain with a cell type specific fashion. However, whether the cellular expression pattern of SRG is conserved between human and mouse brain is not thoroughly studied.ResultsWe analyzed the single-cell transcription of 180 SRG from human and mouse primary visual cortex (V1). We compared the percentage of glutamatergic, GABAergic and non-neuronal cells that express each SRG between mouse and human V1 cortex. Thirty percent (54/180) of SRG had significantly different expression rate in glutamatergic neurons between mouse and human V1 cortex. By contrast, only 5.6% (10/180) of SRG showed significantly different expression in GABAergic neurons, which is similar with the ratio of SRG (15/180) with species difference in total cell populations. Strikingly, the percentage of non-neuronal cells expressing all SRG are indistinguishable between human and mouse V1 cortex. We further analyzed the biological significance of differentially expressed SRG by gene ontology. The species-different SRG in glutamatergic neurons are highly expressed in dendrite and axon. They are enriched in the biological process of response to stimulus. However, the differentially expressed SRG in GABAergic neurons are enriched in the regulation of organelle organization.ConclusionGABAergic neurons are more conserved in the expression of SRG than glutamatergic neurons while the non-neuronal cells show the species conservation for the expression of all SRG. It should be cautious to use mouse models to study those SRG which show different cellular expression pattern between human and mouse cortex.

Tendon Stem/Progenitor Cells and Their Interactions with Extracellular Matrix and Mechanical Loading.

Tendons are unique connective tissues in the sense that their biological properties are largely determined by their tendon-specific stem cells, extracellular matrix (ECM) surrounding the stem cells, mechanical loading conditions placed on the tendon, and the complex interactions among them. This review is aimed at providing an overview of recent advances in the identification and characterization of tendon stem/progenitor cells (TSPCs) and their interactions with ECM and mechanical loading. In addition, the effects of such interactions on the maintenance of tendon homeostasis and the initiation of tendon pathological conditions are discussed. Moreover, the challenges in further investigations of TSPC mechanobiology in vitro and in vivo are outlined. Finally, future research efforts are suggested, which include using specific gene knockout models and single-cell transcription profiling to enable a broad and deep understanding of the physiology and pathophysiology of tendons.

Enhancer Priming Enables Fast and Sustained Transcriptional Responses to Notch Signaling

SummaryInformation from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts.

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

Constraints on Human CD34+ Cell Fate due to Lentiviral Vectors Can Be Relieved by Valproic Acid.

The initial stages following the in vitro cytokine stimulation of human cord blood CD34+ cells overlap with the period when lentiviral gene transfer is typically performed. Single-cell transcriptional profiling and time-lapse microscopy were used to investigate how the vector-cell crosstalk impacts on the fate decision process. The single-cell transcription profiles were analyzed using a new algorithm, and it is shown that lentiviral transduction during the early stages of stimulation modifies the dynamics of the fate choice process of the CD34+ cells. The cells transduced with a lentiviral vector are biased toward the common myeloid progenitor lineage. Valproic acid, a histone deacetylase inhibitor known to increase the grafting potential of the CD34+ cells, improves the transduction efficiency to almost 100%. The cells transduced in the presence of valproic acid can subsequently undergo normal fate commitment. The higher gene transfer efficiency did not alter the genomic integration profile of the vector. These observations open the way to substantially improving lentiviral gene transfer protocols.

Quantitative Single-Cell Transcript Assessment of Biomarkers Supports Cellular Heterogeneity in the Bovine IVD.

Severe and chronic low back pain is often associated with intervertebral disc (IVD) degeneration. While imposing a considerable socio-economic burden worldwide, IVD degeneration is also severely impacting on the quality of life of affected individuals. Cell-based regenerative medicine approaches have moved into clinical trials, yet IVD cell identities in the mature disc remain to be fully elucidated and tissue heterogeneity exists, requiring a better characterization of IVD cells. The bovine coccygeal IVD is an accepted research model to study IVD mechano-biology and disc homeostasis. Recently, we identified novel IVD biomarkers in the outer annulus fibrosus (AF) and nucleus pulposus (NP) of the mature bovine coccygeal IVD through RNA in situ hybridization (AP-RISH) and z-proportion test. Here we follow up on Lam1, Thy1, Gli1, Gli3, Noto, Ptprc, Scx, Sox2 and Zscan10 with fluorescent RNA in situ hybridization (FL-RISH) and confocal microscopy. This permits sub-cellular transcript localization and the addition of quantitative single-cell derived values of mRNA expression levels to our previous analysis. Lastly, we used a Gaussian mixture modeling approach for the exploratory analysis of IVD cells. This work complements our earlier cell population proportion-based study, confirms the previously proposed biomarkers and indicates even further heterogeneity of cells in the outer AF and NP of a mature IVD.

Use of Single Molecule Fluorescent In Situ Hybridization (SM-FISH) to Quantify and Localize mRNAs in Murine Oocytes.

Current methods routinely used to quantify mRNA in oocytes and embryos include digital reverse-transcription polymerase chain reaction (dPCR), quantitative, real-time RT-PCR (RT-qPCR) and RNA sequencing. When these techniques are performed using a single oocyte or embryo, low-copy mRNAs are not reliably detected. To overcome this problem, oocytes or embryos can be pooled together for analysis; however, this often leads to high variability amongst samples. In this protocol, we describe the use of fluorescence in situ hybridization (FISH) using branched DNA chemistry. This technique identifies the spatial pattern of mRNAs in individual cells. When the technique is coupled with Spot Finding and Trackingcomputer software, the abundance of mRNAs in the cell can also be quantified. Using this technique, there is reduced variability within an experimental group and fewer oocytes and embryos are required to detect significant differences between experimental groups. Commercially available branched-DNA SM-FISH kits have been optimized to detect mRNAs in sectioned tissues or adherent cells on slides. However, oocytes do not effectively adhere to slides and some reagents in the kit were too harsh resulting in oocyte lysis. To prevent this lysis, several modifications were made to the FISH kit. Specifically, oocyte permeabilization and washbuffers designed for the immunofluorescence of oocytes and embryos replaced the proprietary buffers. The permeabilization, washes, and incubations with probes and amplifier were performed in 6-well plates and oocytes were placed on slides at the end of the protocol using mounting media. These modifications were able to overcome the limitations of the commercially available kit, in particular, the oocyte lysis. To accurately and reproducibly count the number of mRNAs in individual oocytes, computer software was used. Together, this protocol represents an alternative to PCR and sequencing to compare the expression of specific transcripts in single cells.

Systems based analysis of human embryos and gene networks involved in cell lineage allocation

BackgroundLittle is understood of the molecular mechanisms involved in the earliest cell fate decision in human development, leading to the establishment of the trophectoderm (TE) and inner cell mass (ICM) stem cell population. Notably, there is a lack of understanding of how transcriptional networks arise during reorganisation of the embryonic genome post-fertilisation.ResultsWe identified a hierarchical structure of preimplantation gene network modules around the time of embryonic genome activation (EGA). Using network models along with eukaryotic initiation factor (EIF) and epigenetic-associated gene expression we defined two sets of blastomeres that exhibited diverging tendencies towards ICM or TE. Analysis of the developmental networks demonstrated stage specific EIF expression and revealed that histone modifications may be an important epigenetic regulatory mechanism in preimplantation human embryos. Comparison to published RNAseq data confirmed that during EGA the individual 8-cell blastomeres are transcriptionally primed for the first lineage decision in development towards ICM or TE.ConclusionsUsing multiple systems biology approaches to compare developmental stages in the early human embryo with single cell transcript data from blastomeres, we have shown that blastomeres considered to be totipotent are not transcriptionally equivalent. Furthermore we have linked the developmental interactome to individual blastomeres and to later cell lineage. This has clinical implications for understanding the impact of fertility treatments and developmental programming of long term health.

Single-Cell Tagged Reverse Transcription (STRT-Seq).

Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5' tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.