scBubbletree: computational approach for visualization of single cell RNA-seq data.

BACKGROUND: Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. RESULTS: We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. CONCLUSIONS: To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/.

Single-cell RNA and T-cell receptor sequencing unveil mycosis fungoides heterogeneity and a possible gene signature.

Background: Mycosis fungoides (MF) is the most common subtype of cutaneous T-cell lymphoma (CTCL). Comprehensive analysis of MF cells in situ and ex vivo is complicated by the fact that is challenging to distinguish malignant from reactive T cells with certainty. Methods: To overcome this limitation, we performed combined single-cell RNA (scRNAseq) and T-cell receptor TCR sequencing (scTCRseq) of skin lesions of cutaneous MF lesions from 12 patients. A sufficient quantity of living T cells was obtained from 9 patients, but 2 had to be excluded due to unclear diagnoses (coexisting CLL or revision to a fixed toxic drug eruption). Results: From the remaining patients we established single-cell mRNA expression profiles and the corresponding TCR repertoire of 18,630 T cells. TCR clonality unequivocally identified 13,592 malignant T cells. Reactive T cells of all patients clustered together, while malignant cells of each patient formed a unique cluster expressing genes typical of naive/memory, such as CD27, CCR7 and IL7R, or cytotoxic T cells, e.g., GZMA, NKG7 and GNLY. Genes encoding classic CTCL markers were not detected in all clusters, consistent with the fact that mRNA expression does not correlate linearly with protein expression. Nevertheless, we successfully pinpointed distinctive gene signatures differentiating reactive malignant from malignant T cells: keratins (KRT81, KRT86), galectins (LGALS1, LGALS3) and S100 genes (S100A4, S100A6) being overexpressed in malignant cells. Conclusions: Combined scRNAseq and scTCRseq not only allows unambiguous identification of MF cells, but also revealed marked heterogeneity between and within patients with unexpected functional phenotypes. While the correlation between mRNA and protein abundance was limited with respect to established MF markers, we were able to identify a single-cell gene expression signature that distinguishes malignant from reactive T cells.

The Fungal Gut Microbiome Exhibits Reduced Diversity and Increased Relative Abundance of Ascomycota in Severe COVID-19 Illness and Distinct Interconnected Communities in SARS-CoV-2 Positive Patients.

Clinical and experimental studies indicate that the bacterial and fungal gut microbiota modulates immune responses in distant organs including the lungs. Immune dysregulation is associated with severe SARS-CoV-2 infection, and several groups have observed gut bacterial dysbiosis in SARS-CoV-2 infected patients, while the fungal gut microbiota remains poorly defined in these patients. We analyzed the fungal gut microbiome from rectal swabs taken prior to anti-infective treatment in 30 SARS-CoV-2 positive (21 non-severe COVID-19 and 9 developing severe/critical COVID-19 patients) and 23 SARS-CoV-2 negative patients by ITS2-sequencing. Pronounced but distinct interconnected fungal communities distinguished SARS-CoV-2 positive and negative patients. Fungal gut microbiota in severe/critical COVID-19 illness was characterized by a reduced diversity, richness and evenness and by an increase of the relative abundance of the Ascomycota phylum compared with non-severe COVID-19 illness. A dominance of a single fungal species with a relative abundance of >75% was a frequent feature in severe/critical COVID-19. The dominating fungal species were highly variable between patients even within the groups. Several fungal taxa were depleted in patients with severe/critical COVID-19.The distinct compositional changes of the fungal gut microbiome in SARS-CoV-2 infection, especially in severe COVID-19 illness, illuminate the necessity of a broader approach to investigate whether the differences in the fungal gut microbiome are consequences of SARS-CoV-2 infection or a predisposing factor for critical illness.

A Pro-Inflammatory Gut Microbiome Characterizes SARS-CoV-2 Infected Patients and a Reduction in the Connectivity of an Anti-Inflammatory Bacterial Network Associates With Severe COVID-19.

The gut microbiota contributes to maintaining human health and regulating immune responses. Severe COVID-19 illness is associated with a dysregulated pro-inflammatory immune response. The effect of SARS-CoV-2 on altering the gut microbiome and the relevance of the gut microbiome on COVID-19 severity needs to be clarified. In this prospective study, we analyzed the gut microbiome of 212 patients of a tertiary care hospital (117 patients infected with SARS-CoV-2 and 95 SARS-CoV-2 negative patients) using 16S rRNA gene sequencing of the V3-V4 region. Inflammatory markers and immune cells were quantified from blood. The gut microbiome in SARS-CoV-2 infected patients was characterized by a lower bacterial richness and distinct differences in the gut microbiome composition, including an enrichment of the phyla Proteobacteria and Bacteroidetes and a decrease of Actinobacteria compared to SARS-CoV-2 negative patients. The relative abundance of several genera including Bifidobacterium, Streptococcus and Collinsella was lower in SARS-CoV-2 positive patients while the abundance of Bacteroides and Enterobacteriaceae was increased. Higher pro-inflammatory blood markers and a lower CD8+ T cell number characterized patients with severe COVID-19 illness. The gut microbiome of patients with severe/critical COVID-19 exhibited a lower abundance of butyrate-producing genera Faecalibacterium and Roseburia and a reduction in the connectivity of a distinct network of anti-inflammatory genera that was observed in patients with mild COVID-19 illness and in SARS-CoV-2 negative patients. Dysbiosis of the gut microbiome associated with a pro-inflammatory signature may contribute to the hyperinflammatory immune response characterizing severe COVID-19 illness.

BRAF and MEK inhibition in melanoma patients enables reprogramming of tumor infiltrating lymphocytes.

BACKGROUND: Combined inhibition of BRAF/MEK is an established therapy for melanoma. In addition to its canonical mode of action, effects of BRAF/MEK inhibitors on antitumor immune responses are emerging. Thus, we investigated the effect of these on adaptive immune responses. PATIENTS, METHODS AND RESULTS: Sequential tumor biopsies obtained before and during BRAF/MEK inhibitor treatment of four (n = 4) melanoma patients were analyzed. Multiplexed immunofluorescence staining of tumor tissue revealed an increased infiltration of CD4+ and CD8+ T cells upon therapy. Determination of the T-cell receptor repertoire usage demonstrated a therapy induced increase in T-cell clonotype richness and diversity. Application of the Grouping of Lymphocyte Interactions by Paratope Hotspots algorithm revealed a pre-existing immune response against melanoma differentiation and cancer testis antigens that expanded preferentially upon therapy. Indeed, most of the T-cell clonotypes found under BRAF/MEK inhibition were already present in lower numbers before therapy. This expansion appears to be facilitated by induction of T-bet and TCF7 in T cells, two transcription factors required for self-renewal and persistence of CD8+ memory T cells. CONCLUSIONS: Our results suggest that BRAF/MEK inhibition in melanoma patients allows an increased expansion of pre-existing melanoma-specific T cells by induction of T-bet and TCF7 in these.

Predominance of Central Memory T Cells with High T-Cell Receptor Repertoire Diversity is Associated with Response to PD-1/PD-L1 Inhibition in Merkel Cell Carcinoma.

PURPOSE: Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer, which can be effectively controlled by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. However, a significant proportion of patients are characterized by primary therapy resistance. Predictive biomarkers for response to immunotherapy are lacking. EXPERIMENTAL DESIGN: We applied Bayesian inference analyses on 41 patients with MCC testing various clinical and biomolecular characteristics to predict treatment response. Further, we performed a comprehensive analysis of tumor tissue-based immunologic parameters including multiplexed immunofluorescence for T-cell activation and differentiation markers, expression of immune-related genes and T-cell receptor (TCR) repertoire analyses in 18 patients, seven objective responders, and 11 nonresponders. RESULTS: Bayesian inference analyses demonstrated that among currently discussed biomarkers only unimpaired overall performance status and absence of immunosuppression were associated with response to therapy. However, in responders, a predominance of central memory T cells and expression of genes associated with lymphocyte attraction and activation was evident. In addition, TCR repertoire usage of tumor-infiltrating lymphocytes (TILs) demonstrated low T-cell clonality, but high TCR diversity in responding patients. In nonresponders, terminally differentiated effector T cells with a constrained TCR repertoire prevailed. Sequential analyses of tumor tissue obtained during immunotherapy revealed a more pronounced and diverse clonal expansion of TILs in responders indicating an impaired proliferative capacity among TILs of nonresponders upon checkpoint blockade. CONCLUSIONS: Our explorative study identified new tumor tissue-based molecular characteristics associated with response to anti-PD-1/PD-L1 therapy in MCC. These observations warrant further investigations in larger patient cohorts to confirm their potential value as predictive markers.

Gut Microbiota in Human Immunodeficiency Virus-Infected Individuals Linked to Coronary Heart Disease.

Background: Human immunodeficiency virus (HIV) infection is an independent risk factor for coronary heart disease (CHD) and is associated with perturbation of the gut microbiota. Methods: We analyzed gut microbiota in 30 HIV-infected individuals with CHD (CHD+) and 30 without CHD (CHD-) of the HIV-HEART study group. Results: Gut microbiota linked to CHD was associated with lower α-diversity. Despite insignificant differences in ß-diversity, co-occurrence networks of bacterial genera clearly diverged between CHD+ and CHD- individuals. Multidimensional scaling separated HIV-infected individuals into 2 microbiome clusters, dominated by the genus Prevotella or Bacteroides. The relative abundance of 49 other genera was significantly different between both clusters. The Prevotella-rich cluster was largely composed of men who have sex with men (MSM) (97%), whereas the Bacteroides-rich cluster comprised both MSM (45%) and heterosexual individuals (55%). MSM of the Bacteroides-rich cluster were characterized by reduced α-diversity, advanced immunological HIV stage, longer antiretroviral therapy with more ART regimens, and longer use of protease inhibitors, compared with Prevotella-rich MSM. Conclusions: Community structures of gut microbiota rather than individual species might facilitate risk assessment of CHD in HIV-infected individuals. Sexual behavior appears to be an important factor affecting gut microbiota ß-diversity and should be considered in future studies.

Trade-off shapes diversity in eco-evolutionary dynamics.

We introduce an Interaction- and Trade-off-based Eco-Evolutionary Model (ITEEM), in which species are competing in a well-mixed system, and their evolution in interaction trait space is subject to a life-history trade-off between replication rate and competitive ability. We demonstrate that the shape of the trade-off has a fundamental impact on eco-evolutionary dynamics, as it imposes four phases of diversity, including a sharp phase transition. Despite its minimalism, ITEEM produces a remarkable range of patterns of eco-evolutionary dynamics that are observed in experimental and natural systems. Most notably we find self-organization towards structured communities with high and sustained diversity, in which competing species form interaction cycles similar to rock-paper-scissors games.

Quantitative Comparison of Abundance Structures of Generalized Communities: From B-Cell Receptor Repertoires to Microbiomes.

The community, the assemblage of organisms co-existing in a given space and time, has the potential to become one of the unifying concepts of biology, especially with the advent of high-throughput sequencing experiments that reveal genetic diversity exhaustively. In this spirit we show that a tool from community ecology, the Rank Abundance Distribution (RAD), can be turned by the new MaxRank normalization method into a generic, expressive descriptor for quantitative comparison of communities in many areas of biology. To illustrate the versatility of the method, we analyze RADs from various generalized communities, i.e. assemblages of genetically diverse cells or organisms, including human B cells, gut microbiomes under antibiotic treatment and of different ages and countries of origin, and other human and environmental microbial communities. We show that normalized RADs enable novel quantitative approaches that help to understand structures and dynamics of complex generalized communities.