Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity.

Understanding the organizational logic of neural circuits requires deciphering the biological basis of neuronal diversity and identity, but there is no consensus on how neuron types should be defined. We analyzed single-cell transcriptomes of a set of anatomically and physiologically characterized cortical GABAergic neurons and conducted a computational genomic screen for transcriptional profiles that distinguish them from one another. We discovered that cardinal GABAergic neuron types are delineated by a transcriptional architecture that encodes their synaptic communication patterns. This architecture comprises 6 categories of ∼40 gene families, including cell-adhesion molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and vesicular release components, and transcription factors. Combinatorial expression of select members across families shapes a multi-layered molecular scaffold along the cell membrane that may customize synaptic connectivity patterns and input-output signaling properties. This molecular genetic framework of neuronal identity integrates cell phenotypes along multiple axes and provides a foundation for discovering and classifying neuron types.

Single-Cell RNA-Seq Mapping of Human Thymopoiesis Reveals Lineage Specification Trajectories and a Commitment Spectrum in T Cell Development.

The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34+ progenitor and the more differentiated CD34- fractions in the human postnatal thymus. CD34+ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7- and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34+ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

A time-resolved single-cell roadmap of the logic driving anterior neural crest diversification from neural border to migration stages.

Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of early molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification toward definitive neural crest. Here, we combine ultradense single-cell transcriptomes with machine-learning and large-scale transcriptomic and epigenomic experimental validation of selected trajectories, to provide the general principles and highlight specific features of the GRN underlying neural crest fate diversification from induction to early migration stages using Xenopus frog embryos as a model. During gastrulation, a transient neural border zone state precedes the choice between neural crest and placodes which includes multiple converging gene programs. During neurulation, transcription factor connectome, and bifurcation analyses demonstrate the early emergence of neural crest fates at the neural plate stage, alongside an unbiased multipotent-like lineage persisting until epithelial-mesenchymal transition stage. We also decipher circuits driving cranial and vagal neural crest formation and provide a broadly applicable high-throughput validation strategy for investigating single-cell transcriptomes in vertebrate GRNs in development, evolution, and disease.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

Single cell transcriptomics reveals UAR codon reassignment in Palmarella salina (Metopida, Armophorea) and confirms Armophorida belongs to APM clade.

Anaerobes have emerged in several major lineages of ciliates, but the number of independent transitions to anaerobiosis among ciliates is unknown. The APM clade (Armophorea, Muranotrichea, Parablepharismea) represents the largest clade of obligate anaerobes among ciliates and contains free-living marine and freshwater representatives as well as gut endobionts of animals. The evolution of APM group has only recently started getting attention, and our knowledge on its phylogeny and genetics is still limited to a fraction of taxa. While ciliates portray a wide array of alternatives to the standard genetic code across numerous classes, the APM ciliates were considered to be the largest group using exclusively standard nuclear genetic code. In this study, we present a pan-ciliate phylogenomic analysis with emphasis on the APM clade, bringing the first phylogenomic analysis of the family Tropidoatractidae (Armophorea) and confirming the position of Armophorida within Armophorea. We include five newly sequenced single cell transcriptomes from marine, freshwater, and endobiotic APM ciliates - Palmarella salina, Anteclevelandella constricta, Nyctotherus sp., Caenomorpha medusula, and Thigmothrix strigosa. We report the first discovery of an alternative nuclear genetic code among APM ciliates, used by Palmarella salina (Tropidoatractidae, Armophorea), but not by its close relative, Tropidoatractus sp., and provide a comparative analysis of stop codon identity and frequency indicating the precedency to the UAG codon loss/reassignment over the UAA codon reassignment in the specific ancestor of Palmarella. Comparative genomic and proteomic studies of this group may help explain the constraints that underlie UAR stop-to-sense reassignment, the most frequent type of alternative nuclear genetic code, not only in ciliates, but eukaryotes in general.

Transcriptomic landscape reveals germline potential of porcine skin-derived multipotent dermal fibroblast progenitors.

According to estimations, approximately about 15% of couples worldwide suffer from infertility, in which individuals with azoospermia or oocyte abnormalities cannot be treated with assisted reproductive technology. The skin-derived stem cells (SDSCs) differentiation into primordial germ cell-like cells (PGCLCs) is one of the major breakthroughs in the field of stem cells intervention for infertility treatment in recent years. However, the cellular origin of SDSCs and their dynamic changes in transcription profile during differentiation into PGCLCs in vitro remain largely undissected. Here, the results of single-cell RNA sequencing indicated that porcine SDSCs are mainly derived from multipotent dermal fibroblast progenitors (MDFPs), which are regulated by growth factors (EGF/bFGF). Importantly, porcine SDSCs exhibit pluripotency for differentiating into three germ layers and can effectively differentiate into PGCLCs through complex transcriptional regulation involving histone modification. Moreover, this study also highlights that porcine SDSC-derived PGCLCs specification exhibit conservation with the human primordial germ cells lineage and that its proliferation is mediated by the MAPK signaling pathway. Our findings provide substantial novel insights into the field of regenerative medicine in which stem cells differentiate into germ cells in vitro, as well as potential therapeutic effects in individuals with azoospermia and/or defective oocytes.

Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization.

Over the past decade, invention and adoption of novel multiplexing technologies for tissues have made increasing impacts in basic and translational research and, to a lesser degree, clinical medicine. Platforms capable of highly multiplexed immunohistochemistry or in situ RNA measurements promise evaluation of protein or RNA targets at levels of plex and sensitivity logs above traditional methods - all with preservation of spatial context. These methods promise objective biomarker quantification, markedly increased sensitivity, and single-cell resolution. Increasingly, development of novel technologies is enabling multi-omic interrogations with spatial correlation of RNA and protein expression profiles in the same sample. Such sophisticated methods will provide unprecedented insights into tissue biology, biomarker science, and, ultimately, patient health. However, this sophistication comes at significant cost, requiring extensive time, practical knowledge, and resources to implement. This review will describe the technical features, advantages, and limitations of currently available multiplexed immunohistochemistry and spatial transcriptomic platforms. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Recent Advances in Pathology: the 2021 Annual Review Issue of The Journal of Pathology.

The 2021 Annual Review Issue of The Journal of Pathology contains 14 invited reviews on current research areas of particular importance in pathology. The subjects included here reflect the broad range of interests covered by the journal, including both basic and applied research fields but always with the aim of improving our understanding of human disease. This year, our reviews encompass the huge impact of the COVID-19 pandemic, the development and application of biomarkers for immune checkpoint inhibitors, recent advances in multiplexing antigen/nucleic acid detection in situ, the use of genomics to aid drug discovery, organoid methodologies in research, the microbiome in cancer, the role of macrophage-stroma interactions in fibrosis, and TGF-ß as a driver of fibrosis in multiple pathologies. Other reviews revisit the p53 field and its lack of clinical impact to date, dissect the genetics of mitochondrial diseases, summarise the cells of origin and genetics of sarcomagenesis, provide new data on the role of TRIM28 in tumour predisposition, review our current understanding of cancer stem cell niches, and the function and regulation of p63. The reviews are authored by experts in their field from academia and industry, and provide comprehensive updates of the chosen areas, in which there has been considerable recent progress. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Cell-specific expression of lung disease risk-related genes in the human small airway epithelium.

BACKGROUND: The human small airway epithelium (SAE) plays a central role in the early events in the pathogenesis of most inherited and acquired lung disorders. Little is known about the molecular phenotypes of the specific cell populations comprising the SAE in humans, and the contribution of SAE specific cell populations to the risk for lung diseases. METHODS: Drop-seq single-cell RNA-sequencing was used to characterize the transcriptome of single cells from human SAE of nonsmokers and smokers by bronchoscopic brushing. RESULTS: Eleven distinct cell populations were identified, including major and rare epithelial cells, and immune/inflammatory cells. There was cell type-specific expression of genes relevant to the risk of the inherited pulmonary disorders, genes associated with risk of chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis and (non-mutated) driver genes for lung cancers. Cigarette smoking significantly altered the cell type-specific transcriptomes and disease risk-related genes. CONCLUSIONS: This data provides new insights into the possible contribution of specific lung cells to the pathogenesis of lung disorders.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data.

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

Murine skin-derived multipotent papillary dermal fibroblast progenitors show germline potential in vitro.

BACKGROUND: Many laboratories have described the in vitro isolation of multipotent cells with stem cell properties from the skin of various species termed skin-derived stem cells (SDSCs). However, the cellular origin of these cells and their capability to give rise, among various cell types, to male germ cells, remain largely unexplored. METHODS: SDSCs were isolated from newborn mice skin, and then differentiated into primordial germ cell-like cells (PGCLCs) in vitro. Single-cell RNA sequencing (scRNA-seq) was then applied to dissect the cellular origin of SDSCs using cells isolated from newborn mouse skin and SDSC colonies. Based on an optimized culture strategy, we successfully generated spermatogonial stem cell-like cells (SSCLCs) in vitro. RESULTS: Here, using scRNA-seq and analyzing the profile of 7543 single-cell transcriptomes from newborn mouse skin and SDSCs, we discovered that they mainly consist of multipotent papillary dermal fibroblast progenitors (pDFPs) residing in the dermal layer. Moreover, we found that epidermal growth factor (EGF) signaling is pivotal for the capability of these progenitors to proliferate and form large colonies in vitro. Finally, we optimized the protocol to efficiently generate PGCLCs from SDSCs. Furthermore, PGCLCs were induced into SSCLCs and these SSCLCs showed meiotic potential when cultured with testicular organoids. CONCLUSIONS: Our findings here identify pDFPs as SDSCs derived from newborn skin and show for the first time that such precursors can be induced to generate cells of the male germline.

PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes.

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at https://github.com/WangX-Lab/PreCanCell.

Immune pathway activation in neurons triggers neural damage after stroke.

Ischemic brain injury is a severe medical condition with high incidences in elderly people without effective treatment for the resulting neural damages. Using a unilateral mouse stroke model, we analyze single-cell transcriptomes of ipsilateral and contralateral cortical penumbra regions to objectively reveal molecular events with single-cell resolution at 4 h and 1, 3, and 7 days post-injury. Here, we report that neurons are among the first cells that sense the lack of blood supplies by elevated expression of CCAAT/enhancer-binding protein ß (C/EBPß). To our surprise, the canonical inflammatory cytokine gene targets for C/EBPß, including interleukin-1ß (IL-1ß) and tumor necrosis factor α (TNF-α), are subsequently induced also in neuronal cells. Neuronal-specific silencing of C/EBPß or IL-1ß and TNF-α substantially alleviates downstream inflammatory injury responses and is profoundly neural protective. Taken together, our findings reveal a neuronal inflammatory mechanism underlying early pathological triggers of ischemic brain injury.

Pre-existing Castration-resistant Prostate Cancer-like Cells in Primary Prostate Cancer Promote Resistance to Hormonal Therapy.

BACKGROUND: Hormonal therapy targeting the androgen receptor inhibits prostate cancer (PCa), but the tumor eventually recurs as castration-resistant prostate cancer (CRPC). OBJECTIVE: To understand the mechanisms by which subclones within early PCa develop into CRPC. DESIGN, SETTING, AND PARTICIPANTS: We isolated epithelial cells from fresh human PCa cases, including primary adenocarcinoma, locally recurrent CRPC, and metastatic CRPC, and utilized single-cell RNA sequencing to identify subpopulations destined to become either CRPC-adeno or small cell neuroendocrine carcinoma (SCNC). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We revealed dynamic transcriptional reprogramming that promotes disease progression among 23226 epithelial cells using single-cell RNA sequencing, and validated subset-specific progression using immunohistochemistry and large cohorts of publically available genomic data. RESULTS AND LIMITATIONS: We identified a small fraction of highly plastic CRPC-like cells in hormone-naïve early PCa and demonstrated its correlation with biochemical recurrence and distant metastasis, independent of clinical characteristics. We show that progression toward castration resistance was initiated from subtype-specific lineage plasticity and clonal expansion of pre-existing neuroendocrine and CRPC-like cells in early PCa. CONCLUSIONS: CRPC-like cells are present early in the development of PCa and are not exclusively the result of acquired evolutionary selection during androgen deprivation therapy. The lethal CRPC and SCNC phenotypes should be targeted earlier in the disease course of patients with PCa. PATIENT SUMMARY: Here, we report the presence of pre-existing castration-resistant prostate cancer (CRPC)-like cells in primary prostate cancer, which represents a novel castration-resistant mechanism different from the adaptation mechanism after androgen deprivation therapy (ADT). Patients whose tumors harbor increased pre-existing neuroendocrine and CRPC-like cells may become rapidly resistant to ADT and may require aggressive early intervention.

Can we infer tumor presence of single cell transcriptomes and their tumor of origin from bulk transcriptomes by machine learning?

There is a growing need to build a model that uses single cell RNA-seq (scRNA-seq) to separate malignant cells from nonmalignant cells and to identify tumor of origin of single cells and/or circulating tumor cells (CTCs). Currently, it is infeasible to build a tumor of origin model learnt from scRNA-seq by machine learning (ML). We then wondered if an ML model learnt from bulk transcriptomes is applicable to scRNA-seq to infer single cells' tumor presence and further indicate their tumor of origin. We used k-nearest neighbors, one-versus-all support vector machine, one-versus-one support vector machine, random forest and introduced scTumorTrace to conduct a pioneering experiment containing leukocytes and seven major cancer types where bulk RNA-seq and scRNA-seq data were available. 13 ML models learnt from bulk RNA-seq were all reliable to use (F-score > 96%) shown by a validation set of bulk transcriptomes, but none of them was applicable to scRNA-seq except scTumorTrace. Making inferences from bulk RNA-seq to scRNA-seq was impaired by feature selection and improved by log2-transformed TPM units. scTumorTrace with transcriptome-wide 2-tuples showed F-score beyond 98.74 and 94.29% in inferring tumor presence and tumor of origin at single-cell resolution and correctly identified 45 single candidate prostate CTCs but lineage-confirmed non-CTCs as leukocytes. We concluded that modern ML techniques are quantitative and could hardly address the raised questions. scTumorTrace with transcriptome-wide 2-tuples is qualitative, standardization-free and not subject to log2-transformed quantities, enabling us to infer tumor presence of single cell transcriptomes and their tumor of origin from bulk transcriptomes.