Rapid Identification of the Tumor-Specific Reactive TIL Repertoire via Combined Detection of CD137, TNF, and IFNγ, Following Recognition of Autologous Tumor-Antigens.

Detecting the entire repertoire of tumor-specific reactive tumor-infiltrating lymphocytes (TILs) is essential for investigating their immunological functions in the tumor microenvironment. Current in vitro assays identifying tumor-specific functional activation measure the upregulation of surface molecules, de novo production of antitumor cytokines, or mobilization of cytotoxic granules following recognition of tumor-antigens, yet there is no widely adopted standard method. Here we established an enhanced, yet simple, method for identifying simultaneously CD8+ and CD4+ tumor-specific reactive TILs in vitro, using a combination of widely known and available flow cytometry assays. By combining the detection of intracellular CD137 and de novo production of TNF and IFNγ after recognition of naturally-presented tumor antigens, we demonstrate that a larger fraction of tumor-specific and reactive CD8+ TILs can be detected in vitro compared to commonly used assays. This assay revealed multiple polyfunctionality-based clusters of both CD4+ and CD8+ tumor-specific reactive TILs. In situ, the combined detection of TNFRSF9, TNF, and IFNG identified most of the tumor-specific reactive TIL repertoire. In conclusion, we describe a straightforward method for efficient identification of the tumor-specific reactive TIL repertoire in vitro, which can be rapidly adopted in most cancer immunology laboratories.

Evaluating genetic causes of azoospermia: What can we learn from a complex cellular structure and single-cell transcriptomics of the human testis?

Azoospermia is a condition defined as the absence of spermatozoa in the ejaculate, but the testicular phenotype of men with azoospermia may be very variable, ranging from full spermatogenesis, through arrested maturation of germ cells at different stages, to completely degenerated tissue with ghost tubules. Hence, information regarding the cell-type-specific expression patterns is needed to prioritise potential pathogenic variants that contribute to the pathogenesis of azoospermia. Thanks to technological advances within next-generation sequencing, it is now possible to obtain detailed cell-type-specific expression patterns in the testis by single-cell RNA sequencing. However, to interpret single-cell RNA sequencing data properly, substantial knowledge of the highly sophisticated data processing and visualisation methods is needed. Here we review the complex cellular structure of the human testis in different types of azoospermia and outline how known genetic alterations affect the pathology of the testis. We combined the currently available single-cell RNA sequencing datasets originating from the human testis into one dataset covering 62,751 testicular cells, each with a median of 2637 transcripts quantified. We show what effects the most common data-processing steps have, and how different visualisation methods can be used. Furthermore, we calculated expression patterns in pseudotime, and show how splicing rates can be used to determine the velocity of differentiation during spermatogenesis. With the combined dataset we show expression patterns and network analysis of genes known to be involved in the pathogenesis of azoospermia. Finally, we provide the combined dataset as an interactive online resource where expression of genes and different visualisation methods can be explored ( https://testis.cells.ucsc.edu/ ).

Qualitative Analysis of Tumor-Infiltrating Lymphocytes across Human Tumor Types Reveals a Higher Proportion of Bystander CD8+ T Cells in Non-Melanoma Cancers Compared to Melanoma.

Background: Human intratumoral T cell infiltrates can be defined by quantitative or qualitative features, such as their ability to recognize autologous tumor antigens. In this study, we reproduced the tumor-T cell interactions of individual patients to determine and compared the qualitative characteristics of intratumoral T cell infiltrates across multiple tumor types. Methods: We employed 187 pairs of unselected tumor-infiltrating lymphocytes (TILs) and autologous tumor cells from patients with melanoma, renal-, ovarian-cancer or sarcoma, and single-cell RNA sequencing data from a pooled cohort of 93 patients with melanoma or epithelial cancers. Measures of TIL quality including the proportion of tumor-reactive CD8+ and CD4+ TILs, and TIL response polyfunctionality were determined. Results: Tumor-specific CD8+ and CD4+ TIL responses were detected in over half of the patients in vitro, and greater CD8+ TIL responses were observed in melanoma, regardless of previous anti-PD-1 treatment, compared to renal cancer, ovarian cancer and sarcoma. The proportion of tumor-reactive CD4+ TILs was on average lower and the differences less pronounced across tumor types. Overall, the proportion of tumor-reactive TILs in vitro was remarkably low, implying a high fraction of TILs to be bystanders, and highly variable within the same tumor type. In situ analyses, based on eight single-cell RNA-sequencing datasets encompassing melanoma and five epithelial cancers types, corroborated the results obtained in vitro. Strikingly, no strong correlation between the proportion of CD8+ and CD4+ tumor-reactive TILs was detected, suggesting the accumulation of these responses in the tumor microenvironment to follow non-overlapping biological pathways. Additionally, no strong correlation between TIL responses and tumor mutational burden (TMB) in melanoma was observed, indicating that TMB was not a major driving force of response. No substantial differences in polyfunctionality across tumor types were observed. Conclusions: These analyses shed light on the functional features defining the quality of TIL infiltrates in cancer. A significant proportion of TILs across tumor types, especially non-melanoma, are bystander T cells. These results highlight the need to develop strategies focused on the tumor-reactive TIL subpopulation.

Integration and reanalysis of transcriptomics and methylomics data derived from blood and testis tissue of men with 47,XXY Klinefelter syndrome indicates the primary involvement of Sertoli cells in the testicular pathogenesis.

Klinefelter syndrome (KS; 47,XXY) is the most common sex chromosomal anomaly and causes a multitude of symptoms. Often the most noticeable symptom is infertility caused by azoospermia with testicular histology showing hyalinization of tubules, germ cells loss, and Leydig cell hyperplasia. The germ cell loss begins early in life leading to partial hyalinization of the testis at puberty, but the mechanistic drivers behind this remain poorly understood. In this systematic review, we summarize the current knowledge on developmental changes in the cellularity of KS gonads supplemented by a comparative analysis of the fetal and adult gonadal transcriptome, and blood transcriptome and methylome of men with KS. We identified a high fraction of upregulated genes that escape X-chromosome inactivation, thus supporting previous hypotheses that these are the main drivers of the testicular phenotype in KS. Enrichment analysis showed overrepresentation of genes from the X- and Y-chromosome and testicular transcription factors. Furthermore, by re-evaluation of recent single cell RNA-sequencing data originating from adult KS testis, we found novel evidence that the Sertoli cell is the most affected cell type. Our results are consistent with disturbed cross-talk between somatic and germ cells in the KS testis, and with X-escapee genes acting as mediators.

General theory for stochastic admixture graphs and F-statistics.

We provide a general mathematical framework based on the theory of graphical models to study admixture graphs. Admixture graphs are used to describe the ancestral relationships between past and present populations, allowing for population merges and migration events, by means of gene flow. We give various mathematical properties of admixture graphs with particular focus on properties of the so-called F-statistics. Also the Wright-Fisher model is studied and a general expression for the loss of heterozygosity is derived.

Powerful Inference with the D-Statistic on Low-Coverage Whole-Genome Data.

The detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups. When working with high-throughput sequencing data, calling genotypes accurately is not always possible; therefore, the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes. We provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors, and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate. We prove that the D-statistic is approximated by a standard normal distribution. Furthermore, we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low and medium sequencing depth (1-10×), and performances are as good as with perfectly called genotypes at a sequencing depth of 2×. We show the reliability of error correction in scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.