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

The \emph{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 \emph{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 generalize communities

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

BackgroundMycosis 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.MethodsTo 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).ResultsFrom 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.ConclusionsCombined 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

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!##!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

Averaged NRADs of gut microbiome data in six age groups.

<p>The number of NRADs per group from youngest to oldest were 9, 18, 55, 64, 34, and 309, respectively. Solid lines are mean NRADs, shaded areas are 90% confidence intervals for the means.</p

Diversity of the <i>V</i>_<i>H</i> region of BCRs.

<p>(A) The human genome contains sets of <i>V</i>_<i>H</i>, <i>D</i>_<i>H</i>, and <i>J</i>_<i>H</i> gene segments. (B) The “variable” <i>V</i>_<i>H</i> segments can be grouped into seven <i>V</i>_<i>H</i> families based on sequence similarity. (C) A genetically diverse pool of B cells is generated by V(D)J recombination. (D) Exposure to antigens induces an adaptation of the BCR repertoire, generating genetic variants and changing the usage pattern of <i>V</i>_<i>H</i> gene segments.</p

Country of origin and age as determinants of gut microbiomes NRADs.

<p>(A) MDS-ordination of NRADs of those 489 gut microbiomes from Malawi/Venezuela (MV) and United States (US) with age information. Small symbols represent individual NRADs, large symbols are averages. Error bars are 90% confidence intervals of the averages. The two coordinates of the MDS plot explain 83% of the NRAD distances. (B) Importance of each of the 4105 NRAD ranks for the random forest classification according to country of origin (MV vs. US). The two peaks around ranks 20 and 200 are the NRAD regions that carry most information about the country of origin.</p