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Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types. • START combines monosynaptic rabies tracing with single-nucleus RNA sequencing • Local cortical circuit connectivity is transcriptomic subclass and subtype specific • Excitatory neurons receive selective input from subtypes of Sst/Pvalb/Vip/Lamp5 neurons • Transcriptomic characterization of inhibitory input neurons reveals new circuit motifs Patiño et al. introduce single transcriptome assisted rabies tracing (START), a new technical approach combining transsynaptic neuronal circuit mapping with transcriptomic classification to achieve precise neuronal characterization. Using START, they identify novel local cortical circuit connectivity motifs between inhibitory neuron subtypes and layer-specific excitatory subclasses. [ABSTRACT FROM AUTHOR]

Significance: Pulsed infrared neural stimulation (INS, 1875 nm) is an emerging neurostimulation technology that delivers focal pulsed heat to activate functionally specific mesoscale networks and holds promise for clinical application. However, little is known about its effect on excitatory and inhibitory cell types in cerebral cortex. Aim: Estimates of summed population neuronal response time courses provide a potential basis for neural and hemodynamic signals described in other studies. Approach: Using two-photon calcium imaging in mouse somatosensory cortex, we have examined the effect of INS pulse train application on hSyn neurons and mDlx neurons tagged with GCaMP6s. Results: We find that, in anesthetized mice, each INS pulse train reliably induces robust response in hSyn neurons exhibiting positive going responses. Surprisingly, mDlx neurons exhibit negative going responses. Quantification using the index of correlation illustrates responses are reproducible, intensity-dependent, and focal. Also, a contralateral activation is observed when INS applied. Conclusions: In sum, the population of neurons stimulated by INS includes both hSyn and mDlx neurons; within a range of stimulation intensities, this leads to overall excitation in the stimulated population, leading to the previously observed activations at distant post-synaptic sites. [ABSTRACT FROM AUTHOR]

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high‐throughput single‐cell fluorescent imaging. However, fluorescent labeling is costly and time‐consuming. We present a computational method called DeepIFC based on the Inception U‐Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single‐cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling. [ABSTRACT FROM AUTHOR]

Rheumatic diseases are a group of highly heterogeneous autoimmune and inflammatory disorders involving multiple systems. Dysfunction of immune and non-immune cells participates in the complex pathogenesis of rheumatic diseases. Therefore, studies on the abnormal activation of cell subtypes provided a specific basis for understanding the pathogenesis of rheumatic diseases, which promoted the accuracy of disease diagnosis and the effectiveness of various treatments. However, there was still a far way to achieve individualized precision medicine as the result of heterogeneity among cell subtypes. To obtain the biological information of cell subtypes, single-cell sequencing, a cutting-edge technology, is used for analyzing their genomes, transcriptomes, epigenetics, and proteomics. Novel results identified multiple cell subtypes in tissues of patients with rheumatic diseases by single-cell sequencing. Consequently, we provide an overview of recent applications of single-cell sequencing in rheumatic disease and cross-tissue to understand the cell subtypes and functions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Single-cell RNA sequencing, as a new generation of mRNA sequencing method, has gradually matured and been applied in ophthalmic research after more than ten years of development. OBJECTIVE: To review the development of single cell RNA sequencing and its application and progress in ophthalmology. METHODS: The relevant articles in PubMed, Sciencedirect, and Web of Science databases were searched by computer. English key words were “single-cell RNA-seq, retina, cornea, gene marker”. The retrieval time was from January 1, 2006 to January 31, 2021. The articles were screened initially through reading and analysis to exclude duplicates and low-relevance articles. Finally, 80 articles were included for results analysis. RESULTS AND CONCLUSION: (1) Single cell RNA sequencing is on the single cell level, of genome, transcriptome, and epigenome high-throughput sequencing analysis technology. Compared with the conventional sequencing method, it can be further applied to study the differences between the cells, explain cell subtype and specific expression genes, to explore the disease occurrence, development and resistance mechanism, and provide new targets for the immune therapy. (2) Currently, there are many branches of single-cell RNA sequencing, such as STRT-seq, Smart-seq, CEL-seq, MARS-seq, Cyto-seq, Drop-seq, inDropseq, and Seq-Well. These technologies have their own advantages and application fields. For example, STRT-seq is only at the 5’ end and does not have chain specificity, whereas Samrt-seq transcriptional coverage is full-length. (3) In the field of ophthalmology, single-cell RNA sequencing is mainly used in the study of cornea and retina. There are new scientific discoveries in the identification of new subtypes of eye cells and the diagnosis of ophthalmic diseases and potential immunotherapy. A few are used in the study of lens diseases and uveal melanoma. (4) The research of single-cell RNA sequencing technology in the field of cornea is mainly about keratoconus disease and corneal epithelial cell gene expression. Notch1 and PLLP genes were proven to be related to the pathogenesis of keratoconus. The expression of Krt15, Fzd7, and Krt17 genes increased in corneal basal cells, while Sox9, Actn1, and Anxa3 were mainly expressed in basal epithelial progenitor cells. In addition, retinal tissue is a kind of tissue that is easy to obtain and observe. In recent years, single-cell RNA sequencing technology has been applied to study the gene expression of normal retinal tissue cell subtypes and retinal-related diseases, such as age-related macular degeneration and retinoblastoma. (5) Single-cell RNA sequencing technology can accurately reflect the gene expression of a single cell. In recent years, it has been used to study the gene expression, cell subtypes and signaling pathways of cornea and retina, and will play an important role in the treatment of ophthalmic diseases in the future. [ABSTRACT FROM AUTHOR]

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into "understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME)," which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy. [ABSTRACT FROM AUTHOR]

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into “understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME),” which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy.

Significance: Pulsed infrared neural stimulation (INS, 1875 nm) is an emerging neurostimulation technology that delivers focal pulsed heat to activate functionally specific mesoscale networks and holds promise for clinical application. However, little is known about its effect on excitatory and inhibitory cell types in cerebral cortex., Aim: Estimates of summed population neuronal response time courses provide a potential basis for neural and hemodynamic signals described in other studies., Approach: Using two-photon calcium imaging in mouse somatosensory cortex, we have examined the effect of INS pulse train application on hSyn neurons and mDlx neurons tagged with GCaMP6s., Results: We find that, in anesthetized mice, each INS pulse train reliably induces robust response in hSyn neurons exhibiting positive going responses. Surprisingly, mDlx neurons exhibit negative going responses. Quantification using the index of correlation illustrates responses are reproducible, intensity-dependent, and focal. Also, a contralateral activation is observed when INS applied., Conclusions: In sum, the population of neurons stimulated by INS includes both hSyn and mDlx neurons; within a range of stimulation intensities, this leads to overall excitation in the stimulated population, leading to the previously observed activations at distant post-synaptic sites., (© 2024 The Authors.)

Background: Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods. Results: We propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github (https://github.com/ShengquanChen/VPAC). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages. Conclusions: VPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data. [ABSTRACT FROM AUTHOR]

In this study, we integrated single-cell RNA sequencing (scRNA-seq) data to investigate cell heterogeneity and utilized MSigDB and CIBERSORTx to explore the pathways of major cell types and the relationships between different cell subtypes. Subsequently, we explored the correlation of cell subtypes with survival and used Gene Set Enrichment Analysis (GSEA) analyses to assess the pathways associated with the infiltration of specific cell subtypes. Finally, multiplex immunohistochemistry in tissue microarray cohort were performed to validate differences in protein level and their correlation with survival. iCCA presented a unique immune ecosystem, with increased proportions of Epi (epithelial)-SPP1–2, Epi-S100P-1, Epi-DN (double negative for SPP1 and S100P expression)-1, Epi-DN-2, Epi-DP (double positive for SPP1 and S100P expression)-1, Plasma B-3, Plasma B-2, B-HSPA1A-1, B-HSPA1A-2 cells, and decreased proportions of B-MS4A1. High level of Epi-DN-2, Epi-SPP1–1, Epi-SPP1–2, B-MS4A1, and low level of Epi-DB-1, Epi-S100P-1, and Epi-S100P-2 was significantly associated with longer overall survival (OS), and high level of B-MS4A1_Low_Epi-DN-2_Low was associated with the shortest OS. Moreover, the results of MsigDB and GSEA suggest that bile acid metabolism is a crucial process in iCCA. Finally, we found that S100P+, SPP1+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ were highly expressed, whereas MS4A1 was lowly expressed in iCCA, and patients with high level of S100P+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ exhibited shorter survival. We identified the cell heterogeneity of iCCA, found that iCCA is a unique immune ecosystem with many cell subtypes, and showed that the novel cell subtypes of SPP1 + S100P+ and MS4A1-SPP1 + S100P+ were key subpopulations in iCCA. • iCCA presented a unique immune ecosystem, with increased proportions of Epi (epithelial)-SPP1–2, Epi-S100P-1, Epi-DN (double negative for SPP1 and S100P expression)-1, Epi-DN-2, Epi-DP (double positive for SPP1 and S100P expression)-1, Plasma B-3, Plasma B-2, B-HSPA1A-1, B-HSPA1A-2 cells, and decreased proportions of B-MS4A1. • High level of Epi-DN-2, Epi-SPP1–1, Epi-SPP1–2, B-MS4A1, and low level of Epi-DB-1, Epi-S100P-1, and Epi-S100P-2 was significantly associated with longer overall survival (OS), and high level of B-MS4A1_Low_Epi-DN-2_Low was associated with the shortest OS. • Through the verified experimental results of the tissue microarray cohort, S100P+, SPP1+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ were highly expressed, whereas MS4A1 was lowly expressed in iCCA, and patients with high level of S100P+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ exhibited shorter survival. [ABSTRACT FROM AUTHOR]

Rheumatic diseases are a group of highly heterogeneous autoimmune and inflammatory disorders involving multiple systems. Dysfunction of immune and non-immune cells participates in the complex pathogenesis of rheumatic diseases. Therefore, studies on the abnormal activation of cell subtypes provided a specific basis for understanding the pathogenesis of rheumatic diseases, which promoted the accuracy of disease diagnosis and the effectiveness of various treatments. However, there was still a far way to achieve individualized precision medicine as the result of heterogeneity among cell subtypes. To obtain the biological information of cell subtypes, single-cell sequencing, a cutting-edge technology, is used for analyzing their genomes, transcriptomes, epigenetics, and proteomics. Novel results identified multiple cell subtypes in tissues of patients with rheumatic diseases by single-cell sequencing. Consequently, we provide an overview of recent applications of single-cell sequencing in rheumatic disease and cross-tissue to understand the cell subtypes and functions., Competing Interests: Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper., (copyright: © 2022 Ding et al.)

Heterogeneous human amniotic fluid contains various cell types. Herein, we report on the possibility of simultaneously isolating three subtypes of cells from one primary culture. Using a stainless steel instrument named a colony poculum, two of the three cell subtypes could be efficiently cultured, and these were further characterized. The results indicated that these two cell subtypes had different morphologies and were characterized by different cell marker expression profiles, including the differential expression of CD105, CD117 and EBAF. Furthermore, their gene expression array data revealed their different gene expression profiles. Although both cell types expressed several embryonic stem cell-specific markers, they were non-tumorigenic in vivo. This paper not only provides new insight into the heterogeneity of human amniotic fluid, it also presents a simple yet efficient cell isolation method. These results will contribute to the thorough investigation of the properties and potential future applications of human amniotic fluid-derived cells. [ABSTRACT FROM AUTHOR]

The mammalian placenta consists of a set of cells to ensure normal placental functions throughout gestation. Dysfunctional placentae are considered as the origin of a series of pregnancy complications. Therefore, it is urgent for detailed information about the molecular recipes of the cell types within the normal placenta. In the past years, gene expression analysis via single-cell RNA-seq (scRNA-seq) offers opportunities to identify new cell types in a variety of organs and tissues. In this study, scRNA-seq was used to explore the cell heterogeneity within the E10.5 mouse placenta and unravel their discrepancies in cell composition and communications. We identified sixteen cell clusters, including some cell clusters that originated from the maternal tissue. Moreover, we traced the developmental trajectories of trophoblasts and Hofbauer-like cells. Further analysis revealed cell connections between the endothelial cells and pericytes, syncytiotrophoblasts, as well as decidual cells. Besides, we highlighted several signaling pathways, such as the EGF, FGF, canonical, and non-canonical WNT signaling pathways, which mediated the potential crosstalk between different cell types within placenta. Our research provides an in-depth understanding of placental development, cellular composition, and communications at the maternal-fetal interface. [ABSTRACT FROM AUTHOR]

Biochemical sensing probes based on aggregation-induced-emission luminogens (AIEgens) are widely used in biological imaging and therapy, chemical sensing, and material sciences. However, it is still a great challenge to quantify the targets through fluorescence intensity of AIEgen probes due to their undesirable aggregations. Here, a PyTPA-ZGO probe with three lifetime signals for precise quantification of furin is constructed: the lifetime signal 1 and signal 2 comes from AIEgen PyTPA-P (τ Pn ) and inorganic nanoparticles Zn 2 GeO 4 :Mn 2+ -NH 2 (τ Zn ), respectively, while the lifetime signal 3 is marked as the composite dual-lifetime signal (CDLS n , C D L S n = τ Z n τ P n ). In contrast, the fluorescence intensity signal of PyTPA-P shows defectively quantitative performance. Furthermore, it is found that the CDLS n exhibits higher significant differences than the two other lifetime signals (τ Pn and τ Zn ) thanks to its wide range between the maximum and minimum signal values and small standard deviation. Therefore, CDLS n is further used to accurately identify cell subtypes based on the specific concentration of furin in each subtype. The lifetime criterion can realize precise quantification, and it should be a promising direction of AIEgen-based quantitative analysis in the future., (© 2021 Wiley-VCH GmbH.)

[Display omitted] • Controllable synthesis of Au@PBA@Apt NPs was succeeded for duplexing detection. • Aptamer-conjugated nanoparticles were specifically targeted to biomarkers of cells. • This method has great potential in cancer cell diagnosis and cell subtypes profiling. Multiplex surface-enhanced Raman scattering (SERS) detection of markers without background in tumor biosystems has its superiority over other optical methods. Herein, we reported a strategy of quantitative discrimination of two breast cancer cell subtypes. Based on our previous studies, two kinds of Prussian blue analogue coated gold nanoparticles (Au@PBA NPs) were designed and synthesized by the replacement of Fe2+ with Pb2+ or Cu2+. Therefore, two distinct SERS emissions of C≡N bonds at 2122 cm−1 and 2176 cm−1 have been acquired. When modified with aptamers of epithelial cell adhesion molecule (EpCAM) and epidermal growth factor receptor (EGFR), which are both expressed in MCF-7 and MDA-MB-231 cell lines but in different levels, the SERS nanoprobes simultaneously identified the relative expression of these biomarkers on the cell surface, providing a good example for ratiometric detection in biosystems without any interference. Each surface marker of tumor cells corresponds to a single SERS emission. Thus, each subtype could be described in a molecular profiling way through duplex C≡N bonds-based SERS emission, which is more advanced than traditional flow cytometry method. [ABSTRACT FROM AUTHOR]

The diverse tumor cell populations may be the critical roles in relapse and resistance to treatment in prostate cancer patients. This study aimed to identify new marker genes and cell subtypes among castration-resistant prostate cancer (CRPC) cells. We downloaded single-cell RNA seq profiles (GSE67980) from the Gene Expression Omnibus (GEO) database. Principal component (PC) analysis and t-Distributed Stochastic Neighbor Embedding (TSNE) analysis were performed to identify marker genes. CRPC cells were clustered and annotated. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses among marker genes were performed. A total of 1500 genes with larger standardized variance were obtained. The top 20 genes were demonstrated in each identified 20 PCs. PC with P-value < 0.05 was selected, including PC1, PC7, PC8, and PC14. The TSNE analysis classified cells as two clusters. The top 6 genes in cluster 0 included HBB, CCL5, SLITRK4, GZMB, BBIP1, and PF4V1. Plus, the top 6 genes in cluster 1 included MLEC, CCT8, CCT3, EPCAM, TMPRSS2, EIF4G2. The GO analysis revealed that these marker genes were mainly enriched in RNA catabolic process, translational initiation, mitochondrial inner membrane, cytosolic part, ribosome, cell adhesion molecule binding, cadherin binding, and structural constituent of ribosome. The KEGG analysis showed that these marker genes mainly enriched in metabolism associated pathways, including carbon metabolism, cysteine and methionine metabolism, propanoate metabolism, pyruvate metabolism, and citrate cycle pathways. To conclude, our results provide essential insights into the spectrum of cellular heterogeneity within human CRPC cells. These marker genes, GO terms and pathways may be critical in the development and progression of human CRPC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)