@article{pmid38577799,

title = {The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis},

author = {Eisa Mahyari and Katinka A Vigh-Conrad and Clément Daube and Ana C Lima and Jingtao Guo and Douglas T Carrell and James M Hotaling and Kenneth I Aston and Donald F Conrad},

doi = {10.1111/andr.13637},

issn = {2047-2927},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {Andrology},

abstract = {BACKGROUND: Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community.nnOBJECTIVE: To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis.nnMETHODS: We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers.nnRESULTS: A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing.nnDISCUSSION AND CONCLUSIONS: HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.},

keywords = {Human Reproduction, Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37267208,

title = {Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation},

author = {Daniel P Lewinsohn and Katinka A Vigh-Conrad and Donald F Conrad and Cory B Scott},

doi = {10.1093/bioinformatics/btad360},

issn = {1367-4811},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Bioinformatics},

volume = {39},

number = {6},

abstract = {MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective.nnRESULTS: We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data.nnAVAILABILITY AND IMPLEMENTATION: Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP.},

keywords = {Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34626582,

title = {Comparative single-cell analysis of biopsies clarifies pathogenic mechanisms in Klinefelter syndrome},

author = {Eisa Mahyari and Jingtao Guo and Ana C Lima and Daniel P Lewinsohn and Alexandra M Stendahl and Katinka A Vigh-Conrad and Xichen Nie and Liina Nagirnaja and Nicole B Rockweiler and Douglas T Carrell and James M Hotaling and Kenneth I Aston and Donald F Conrad},

doi = {10.1016/j.ajhg.2021.09.001},

issn = {1537-6605},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {Am J Hum Genet},

volume = {108},

number = {10},

pages = {1924--1945},

abstract = {Klinefelter syndrome (KS), also known as 47, XXY, is characterized by a distinct set of physiological abnormalities, commonly including infertility. The molecular basis for Klinefelter-related infertility is still unclear, largely because of the cellular complexity of the testis and the intricate endocrine and paracrine signaling that regulates spermatogenesis. Here, we demonstrate an analysis framework for dissecting human testis pathology that uses comparative analysis of single-cell RNA-sequencing data from the biopsies of 12 human donors. By comparing donors from a range of ages and forms of infertility, we generate gene expression signatures that characterize normal testicular function and distinguish clinically distinct forms of male infertility. Unexpectedly, we identified a subpopulation of Sertoli cells within multiple individuals with KS that lack transcription from the XIST locus, and the consequence of this is increased X-linked gene expression compared to all other KS cell populations. By systematic assessment of known cell signaling pathways, we identify 72 pathways potentially active in testis, dozens of which appear upregulated in KS. Altogether our data support a model of pathogenic changes in interstitial cells cascading from loss of X inactivation in pubertal Sertoli cells and nominate dosage-sensitive factors secreted by Sertoli cells that may contribute to the process. Our findings demonstrate the value of comparative patient analysis in mapping genetic mechanisms of disease and identify an epigenetic phenomenon in KS Sertoli cells that may prove important for understanding causes of infertility and sex chromosome evolution.},

keywords = {Human Reproduction, Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31237565,

title = {Unified single-cell analysis of testis gene regulation and pathology in five mouse strains},

author = {Min Jung and Daniel Wells and Jannette Rusch and Suhaira Ahmad and Jonathan Marchini and Simon R Myers and Donald F Conrad},

doi = {10.7554/eLife.43966},

issn = {2050-084X},

year  = {2019},

date = {2019-06-01},

journal = {Elife},

volume = {8},

abstract = {To fully exploit the potential of single-cell functional genomics in the study of development and disease, robust methods are needed to simplify the analysis of data across samples, time-points and individuals. Here we introduce a model-based factor analysis method, SDA, to analyze a novel 57,600 cell dataset from the testes of wild-type mice and mice with gonadal defects due to disruption of the genes , ,  or . By jointly analyzing mutant and wild-type cells we decomposed our data into 46 components that identify novel meiotic gene-regulatory programs, mutant-specific pathological processes, and technical effects, and provide a framework for imputation. We identify, de novo, DNA sequence motifs associated with individual components that define temporally varying modes of gene expression control. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of  and  mice, an area typically associated with immune privilege.},

keywords = {Human Reproduction, Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28745623,

title = {A Standardized Approach for Multispecies Purification of Mammalian Male Germ Cells by Mechanical Tissue Dissociation and Flow Cytometry},

author = {Ana C Lima and Min Jung and Jannette Rusch and Abul Usmani and Alexandra M Lopes and Donald F Conrad},

doi = {10.3791/55913},

issn = {1940-087X},

year  = {2017},

date = {2017-07-01},

urldate = {2017-07-01},

journal = {J Vis Exp},

number = {125},

abstract = {Fluorescence-activated cell sorting (FACS) has been one of the methods of choice to isolate enriched populations of mammalian testicular germ cells. Currently, it allows the discrimination of up to 9 murine germ cell populations with high yield and purity. This high-resolution in discrimination and purification is possible due to unique changes in chromatin structure and quantity throughout spermatogenesis. These patterns can be captured by flow cytometry of male germ cells stained with fluorescent DNA-binding dyes such as Hoechst-33342 (Hoechst). Herein is a detailed description of a recently developed protocol to isolate mammalian testicular germ cells. Briefly, single cell suspensions are generated from testicular tissue by mechanical dissociation, double stained with Hoechst and propidium iodide (PI) and processed by flow cytometry. A serial gating strategy, including the selection of live cells (PI negative) with different DNA content (Hoechst intensity), is used during FACS sorting to discriminate up to 5 germ cell types. These include, with corresponding average purities (determined by microscopy evaluation): spermatogonia (66%), primary (71%) and secondary (85%) spermatocytes, and spermatids (90%), further separated into round (93%) and elongating (87%) subpopulations. Execution of the entire workflow is straightforward, allows the isolation of 4 cell types simultaneously with the appropriate FACS machine, and can be performed in less than 2 h. As reduced processing time is crucial to preserve the physiology of ex vivo cells, this method is ideal for downstream high-throughput studies of male germ cell biology. Moreover, a standardized protocol for multispecies purification of mammalian germ cells eliminates methodological sources of variables and allows a single set of reagents to be used for different animal models.},

keywords = {Human Reproduction, Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid27557646,

title = {Multispecies Purification of Testicular Germ Cells},

author = {Ana C Lima and Min Jung and Jannette Rusch and Abul Usmani and Alexandra Lopes and Donald F Conrad},

doi = {10.1095/biolreprod.116.140566},

issn = {1529-7268},

year  = {2016},

date = {2016-10-01},

urldate = {2016-10-01},

journal = {Biol Reprod},

volume = {95},

number = {4},

pages = {85},

abstract = {Advanced methods of cellular purification are required to apply genome technology to the study of spermatogenesis. One approach, based on flow cytometry of murine testicular cells stained with Hoechst-33342 (Ho-FACS), has been extensively optimized and currently allows the isolation of 9 germ cell types. This staining technique is straightforward to implement, highly effective at purifying specific germ cell types and yields sufficient cell numbers for high throughput studies. Ho-FACS is a technique that does not require species-specific markers, but whose applicability to other species is largely unexplored. We hypothesized that, due to the similar cell physiology of spermatogenesis across mammals, Ho-FACS could be used to produce highly purified subpopulations of germ cells in mammals other than mouse. To test this hypothesis, we applied Ho-FACS to 4 mammalian species that are widely used in testis research - Rattus norvegicus, Cavia porcellus, Canis familiaris and Sus scrofa domesticus We successfully isolated 4 germ cell populations from these species with average purity of 79% for spermatocytes, and 90% for spermatids and 66% for spermatogonia. Additionally, we compare the performance of mechanical and chemical dissociation for each species, and propose an optimized gating strategy to better discriminate round and elongating spermatids in the mouse, which can potentially be applied to other species. Our work indicates that spermatogenesis may be uniquely accessible among mammalian developmental systems, as a single set of reagents may be sufficient to isolate germ cell populations from many different mammalian species, opening new avenues in the fields of development and male reproductive biology.},

keywords = {Human Reproduction, Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}

@article{pmid24759149,

title = {Validating single-cell genomics for the study of renal development},

author = {Sanjay Jain and Michiel J Noordam and Masato Hoshi and Francesco L Vallania and Donald F Conrad},

doi = {10.1038/ki.2014.104},

issn = {1523-1755},

year  = {2014},

date = {2014-11-01},

urldate = {2014-11-01},

journal = {Kidney Int},

volume = {86},

number = {5},

pages = {1049--1055},

abstract = {Single-cell genomics will enable studies of the earliest events in kidney development, although it is unclear if existing technologies are mature enough to generate accurate and reproducible data on kidney progenitors. Here we designed a pilot study to validate a high-throughput assay to measure the expression levels of key regulators of kidney development in single cells isolated from embryonic mice. Our experiment produced 4608 expression measurements of 22 genes, made in small cell pools, and 28 single cells purified from the RET-positive ureteric bud. There were remarkable levels of concordance with expression data generated by traditional microarray analysis on bulk ureteric bud tissue with the correlation between our average single-cell measurements and GUDMAP measurements for each gene of 0.82-0.85. Nonetheless, a major motivation for single-cell technology is to uncover dynamic biology hidden in population means. There was evidence for extensive and surprising variation in expression of Wnt11 and Etv5, both downstream targets of activated RET. The variation for all genes in the study was strongly consistent with burst-like promoter kinetics. Thus, our results can inform the design of future single-cell experiments, which are poised to provide important insights into kidney development and disease.},

keywords = {Single Cell Genomics},

pubstate = {published},

tppubtype = {article}

}