Cell type classification for multi-sample multi-condition comparisons in single-cell RNA sequencing data

Multicellular organisms require specialized cell types in order to function. While a widely accepted definition does not exist, cell types are regarded as groups of cells with similar properties, such as RNA expression, protein abundance and epigenetic modification. Single-cell RNA sequencing (scRNAseq) is a recent breakthrough for explor- ing cell types, providing expression estimates for all genes in thousands of individual cells. Using data-driven algorithms, such as unsupervised clus- tering, scRNAseq has discovered new cell types and created large reference data sets, next to other exploratory achievements. More recently, scRNA- seq was applied to patient cohorts that include different groups, for example disease and healthy or disease subtypes. These multi-sample multi-condition data sets enable statistical inferences between groups, such as differential ex- pression testing. In contrast to projects exploring unknown tissues or species, patient cohorts often study known cell types defined by specific marker genes. Here, I present Pooled Count Poisson Classification (PCPC), a novel cell type classification approach designed for inference with multi-sample multi- condition scRNAseq data sets. PCPC implements a statistical model that allows researchers to distinguish cells according to marker-based cell type definitions, enabling reproducible and comparable analysis between data sets and technologies (e.g. scRNAseq and flow cytometry). Specifically, PCPC pools marker gene counts across related cells to overcome technical noise, and compares them to a user-defined threshold using the Poisson model. In this work, I apply PCPC to three different data sets to demonstrate its utility. The first application shows it is able to annotate all lineages in data from human cord blood mononuclear cells (CBMCs), with a single marker gene per cell type. The second application shows PCPC is able to discriminate fine cell type sub- sets, using data from a human tumor of mucosa-associated lymphoid tissue (MALT). Many cell types in the MALT tumor microenvironment, and T cell subsets in particular, are transcriptionally related, making their classification difficult. In spite of this challenging complexity, PCPC can even use lowly expressed marker genes, such as FOXP3 marking CD3E + CD4 + FOXP3 + reg- ulatory T (T reg ) cells. Furthermore, I find T reg cells isolated from the MALT tumor can further be subdivided into CCR7 + and ICOS + subsets, indicating a mixture of naive-like and activated T reg cells. In comparison to unsuper- vised clustering and the marker-based tool Garnett, classification with PCPC has more flexibility and fewer misclassifications, respectively. Thus, PCPC removes obstacles in studying complex tissues with scRNAseq, such as the microenvironment in human tumors. Furthermore, I demonstrate a multi-sample multi-condition comparison using data from a patient cohort of aggressive and indolent lymphoma subtypes. PCPC is applied to classify CD3E + CD8B + cytotoxic T cells, followed by differential expression testing between the aggressive and indolent subtypes. This uncovers significantly lower LGALS1 expression in indolent tumors, further implicating this gene in tumor aggressiveness and T cell inhibition. Currently, PCPC requires data generated with unique molecular identifiers (UMI), as well as substantial manual work. Due to its ability to resolve com- plex tissues with few marker genes, PCPC may bring clarity to transcrip- tomic cell type definitions and prove useful for multi-sample multi-condition comparisons in scRNAseq data

Neurofilament light and heterogeneity of disease progression in amyotrophic lateral sclerosis: development and validation of a prediction model to improve interventional trials

International audienceBackgroundInterventional trials in amyotrophic lateral sclerosis (ALS) suffer from the heterogeneity of the disease as it considerably reduces statistical power. We asked if blood neurofilament light chains (NfL) could be used to anticipate disease progression and increase trial power.MethodsIn 125 patients with ALS from three independent prospective studies—one observational study and two interventional trials—we developed and externally validated a multivariate linear model for predicting disease progression, measured by the monthly decrease of the ALS Functional Rating Scale Revised (ALSFRS-R) score. We trained the prediction model in the observational study and tested the predictive value of the following parameters assessed at diagnosis: NfL levels, sex, age, site of onset, body mass index, disease duration, ALSFRS-R score, and monthly ALSFRS-R score decrease since disease onset. We then applied the resulting model in the other two study cohorts to assess the actual utility for interventional trials. We analyzed the impact on trial power in mixed-effects models and compared the performance of the NfL model with two currently used predictive approaches, which anticipate disease progression using the ALSFRS-R decrease during a three-month observational period (lead-in) or since disease onset (ΔFRS).ResultsAmong the parameters provided, the NfL levels (P < 0.001) and the interaction with site of onset (P < 0.01) contributed significantly to the prediction, forming a robust NfL prediction model (R = 0.67). Model application in the trial cohorts confirmed its applicability and revealed superiority over lead-in and ΔFRS-based approaches. The NfL model improved statistical power by 61% and 22% (95% confidence intervals: 54%–66%, 7%–29%).ConclusionThe use of the NfL-based prediction model to compensate for clinical heterogeneity in ALS could significantly increase the trial power.NCT00868166, registered March 23, 2009; NCT02306590, registered December 2, 2014

Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues

Regulatory T cells (T-reg cells) perform two distinct functions: they maintain self-tolerance, and they support organ homeostasis by differentiating into specialized tissue T-reg cells. We found that epigenetic modifications defined the molecular characteristics of tissue T-reg cells. Tagmentation-based whole-genome bisulfite sequencing revealed more than 11,000 regions that were methylated differentially in pairwise comparisons of tissue T-reg cell populations and lymphoid T cells. Similarities in the epigenetic landscape led to the identification of a common tissue T-reg cell population that was present in many organs and was characterized by gain and loss of DNA methylation that included many gene sites associated with the T(H)2 subset of helper T cells, such as the gene encoding cytokine IL-33 receptor ST2, as well as the production of tissue-regenerative factors. Furthermore, the ST2-expressing population was dependent on the transcriptional regulator BATF and could be expanded by IL-33. Thus, tissue T-reg cells integrate multiple waves of epigenetic reprogramming that define their tissue-restricted specialization

Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy

Dissecting intratumor heterogeneity of nodal B cell lymphomas at transcriptional, genetic, and drug response levels

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of nodal B cell lymphoma by single cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug response and genomic profiles. Malignant subpopulations of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B cell lymphoma and highlight the relevance of intratumor heterogeneity for personalized cancer therapy

The fungal ligand chitin directly binds TLR2 and triggers inflammation dependent on oligomer size

Chitin is the second most abundant polysaccharide in nature and linked to fungal infection and asthma. However, bona fide immune receptors directly binding chitin and signaling immune activation and inflammation have not been clearly identified because polymeric crude chitin with unknown purity and molecular composition has been used. By using defined chitin (N-acetyl-glucosamine) oligomers, we here identify six-subunit-long chitin chains as the smallest immunologically active motif and the innate immune receptor Toll-like receptor (TLR2) as a primary fungal chitin sensor on human and murine immune cells. Chitin oligomers directly bind TLR2 with nanomolar affinity, and this fungal TLR2 ligand shows overlapping and distinct signaling outcomes compared to known mycobacterial TLR2 ligands. Unexpectedly, chitin oligomers composed of five or less subunits are inactive, hinting to a size-dependent system of immuno-modulation that appears conserved in plants and humans. Since blocking of the chitin-TLR2 interaction effectively prevents chitin-mediated inflammation in vitro and in vivo, our study highlights the chitin-TLR2 interaction as a potential target for developing novel therapies in chitin-related pathologies and fungal disease

The fungal ligand chitin directly binds TLR2 and triggers inflammation dependent on oligomer size.

Chitin is the second most abundant polysaccharide in nature and linked to fungal infection and asthma. However, bona fide immune receptors directly binding chitin and signaling immune activation and inflammation have not been clearly identified because polymeric crude chitin with unknown purity and molecular composition has been used. By using defined chitin (N-acetyl-glucosamine) oligomers, we here identify six-subunit-long chitin chains as the smallest immunologically active motif and the innate immune receptor Toll-like receptor (TLR2) as a primary fungal chitin sensor on human and murine immune cells. Chitin oligomers directly bind TLR2 with nanomolar affinity, and this fungal TLR2 ligand shows overlapping and distinct signaling outcomes compared to known mycobacterial TLR2 ligands. Unexpectedly, chitin oligomers composed of five or less subunits are inactive, hinting to a size-dependent system of immuno-modulation that appears conserved in plants and humans. Since blocking of the chitin-TLR2 interaction effectively prevents chitin-mediated inflammation in vitro and in vivo, our study highlights the chitin-TLR2 interaction as a potential target for developing novel therapies in chitin-related pathologies and fungal disease