Expression of mini-G proteins specifically halt cognate GPCR trafficking and intracellular signalling

Mini-G proteins are engineered thermostable variants of Gα subunits designed to specifically stabilise G protein-coupled receptors (GPCRs) in their active conformation for structural analyses. Due to their smaller size and ease of use, they have become popular tools in recent years to assess specific GPCR behaviours in cells, both as reporters of receptor coupling to each G protein subtype and for in-cell assays designed to quantify compartmentalised receptor signalling from a range of subcellular locations. Here, we describe a previously unappreciated consequence of the co-expression of mini-G proteins with their cognate GPCRs, namely a profound disruption in GPCR trafficking and intracellular signalling caused by the co-expression of the specific mini-G subtype coupled to the affected receptor. We studied the Gαs-coupled pancreatic beta cell class B GPCR glucagon-like peptide-1 receptor (GLP-1R) as a model to describe in detail the molecular consequences derived from this effect, including a complete halt in β-arrestin-2 recruitment and receptor internalisation, despite near-normal levels of receptor GRK2 recruitment and lipid nanodomain segregation, as well as the disruption of endosomal GLP-1R signalling by mini-Gs co-expression. We also extend our analysis to a range of other prototypical GPCRs covering the spectrum of Gα subtype coupling preferences, to unveil a widely conserved phenomenon of GPCR internalisation blockage by specific mini-G proteins coupled to a particular receptor. Our results have important implications for the design of methods to assess intracellular GPCR signalling. We also present an alternative adapted bystander intracellular signalling assay for the GLP-1R in which we substitute the mini-Gs by a nanobody, Nb37, with specificity for active Gαs:GPCR complexes and no deleterious effect on the capacity for GLP-1R internalisation.

How Reproducible Is the Data from Sysmex DI-60 in Leukopenic Samples?

Digital morphology (DM) analyzers are widely applied in clinical practice. It is necessary to evaluate performances of DM analyzers by focusing on leukopenic samples. We evaluated the analytical performance, including precision, of a Sysmex DI-60 system (Sysmex, Kobe, Japan) on white blood cell (WBC) differentials in leukopenic samples. In a total of 40 peripheral blood smears divided into four groups according to WBC count (normal, mild, moderate, and severe leukopenia; each group n = 10), we evaluated precision of WBC preclassificaiton by DI-60. %coefficients of variation (%CVs) of precision varied for each sample and for each cell class; the fewer cells per slide, the higher %CV. The overall specificity and efficiency were high for all cell classes except plasma cells (95.9–99.9% and 90.0–99.4%, respectively). The largest absolute value of mean difference between DI-60 and manual count in each group was: 10.77, normal; 10.22, mild leukopenia; 19.09, moderate leukopenia; 47.74, severe leukopenia. This is the first study that evaluated the analytical performance of DI-60 on WBC differentials in leukopenic samples as the main subject. DI-60 showed significantly different performance depending on WBC count. DM analyzers should be evaluated separately in leukopenic samples, even if the overall performance was acceptable.

Explainability methods for differential gene analysis of single cell RNA-seq clustering models

Single-cell RNA sequencing (scRNA-seq) produces transcriptomic profiling for individual cells. Due to the lack of cell-class annotations, scRNA-seq is routinely analyzed with unsupervised clustering methods. Because these methods are typically limited to producing clustering predictions (that is, assignment of cells to clusters of similar cells), numerous model agnostic differential expression (DE) libraries have been proposed to identify the genes expressed differently in the detected clusters, as needed in the downstream analysis. In parallel, the advancements in neural networks (NN) brought several model-specific explainability methods to identify salient features based on gradients, eliminating the need for external models. We propose a comprehensive study to compare the performance of dedicated DE methods, with that of explainability methods typically used in machine learning, both model agnostic (such as SHAP, permutation importance) and model-specific (such as NN gradient-based methods). The DE analysis is performed on the results of 3 state-of-the-art clustering methods based on NNs. Our results on 36 simulated datasets indicate that all analyzed DE methods have limited agreement between them and with ground-truth genes. The gradients method outperforms the traditional DE methods, which encourages the development of NN-based clustering methods to provide an out-of-the-box DE capability. Employing DE methods on the input data preprocessed by clustering method outperforms the traditional approach of using the original count data, albeit still performing worse than gradient-based methods.

The Clonotypic BCR IG of CLL Patients Contain Predicted T-Cell Class I Epitopes with Shared Structural Properties

Targeted therapies have revolutionized the treatment of chronic lymphocytic leukemia (CLL) with remarkable overall response rates. Against that, however, CLL remains incurable, indicating a need for novel strategies towards disease control and eventual eradication, including reinvigoration of anti-tumor immune responses. T cells in CLL display an oligoclonal profile and appear selected by restricted antigens, with recent evidence suggesting that the selecting epitopes may lie within the clonotypic B-cell receptor immunoglobulins (BcR IG). Should this prove to be the case, such neoepitopes could be exploited as idiotypic targets for cellular therapy or for peptide vaccine design, aiming to augment response to current treatments. Here we performed ad hoc prediction of putative T-cell class I neoepitopes contained within the clonotypic BcR IGs of CLL patients, with an intended bias towards major stereotyped CLL subsets. We selected 27 patients to represent the full spectrum of CLL: (i) with mutated IGHV genes (M-CLL, n=5), (ii) with unmutated IGHV genes (U-CLL, n=5), (iii) assigned to major stereotyped subsets (subset #1, n=7; subset #2, n=5; subset #4, n=5). RT-PCR was performed for the heavy (H) and the light (K/L) IG chains using subgroup-specific Leader primers for the IGHV/IGK/LV gene and universal primers annealing to the constant domain (IGHG, IGHM, IGKC, IGLC), in order to produce the full-length V-(D)-J gene rearrangement sequence, plus the start of the constant domain. PCR products were subjected to direct double-strand Sanger sequencing with a quality-optimized protocol. The amino acid sequences were subsequently parsed in peptides and subjected to NetMHCpan. The rank score was calculated, considering the 4-digit HLA-A and -B typing for each individual patient. High- and medium-binding peptides (rank score <2%) were selected. Exact matches to germline and/or proteome databases were excluded. Overall, 1,007 predicted neoepitopes were identified. All patients had predicted CD8 + T-cell epitopes within the clonotypic BcR IG, either in the heavy chain (26/27 pts, n=632 epitopes) or the light chain (26/27 pts, n=375 epitopes). The majority of the peptides resulted from somatic hypermutations (SHMs) across the IGHV gene outside the complementarity-determining region 3 (CDR3; n=538, 53.4%). With the exception of few peptides located within the FR4 region (n=11, 0.1%), the remaining (n=458, 45.5%) involved (at least part of) the CDR3, which is particularly relevant given its small length (9-27 aa) within the full sequence (331-660 aa). There was no statistically significant difference in the rank score of peptides involving the CDR3 vs. all others. Peptide clustering assigned most of the predicted neoepitopes (970/1007, 96.3%) in 54 clusters of similar length and amino acid composition. Also, it revealed similar or identical predicted neoepitopes among different patients (30 clusters of two, 10 clusters of three, 8 clusters of four, 4 clusters of five, 1 cluster of six and 1 cluster of eight). Importantly, these clusters involved: (i) shared CDR3 patterns in patients assigned to the same stereotyped subset, but also (ii) subset-specific recurrent SHMs across the rearranged IGHV gene, e.g. G-to-E SHM at position 28 in the VH CDR1 of subset #4, a recurrent SHM in this subset. Also of note, the two most highly populated clusters involved peptides within the VL CDR3, and were biased towards specific subsets; the cluster of eight patients contained 4 patients assigned to subset #1 and the cluster of six patients included 4 patients assigned to subset #2. In conclusion, in silico prediction identified a significant number of putative T-cell class I neoepitopes contained within the clonotypic BcR IG of CLL patients. The majority of these neoepitopes can be assigned to clusters based on amino acid similarity and are shared among different patients. Many of them culminate from subset-specific ('stereotyped') CDR3 patterns or recurrent SHMs, suggesting that the targeted SHM which shapes the CLL BcR IG repertoire may produce immunogenic CD8 + T-cell epitopes. Their actual immunogenicity has to be tested in ex vivo studies, currently underway by our group. Disclosures Anagnostopoulos: Abbvie: Other: clinical trials; Sanofi: Other: clinical trials ; Ocopeptides: Other: clinical trials ; GSK: Other: clinical trials; Incyte: Other: clinical trials ; Takeda: Other: clinical trials ; Amgen: Other: clinical trials ; Janssen: Other: clinical trials; novartis: Other: clinical trials; Celgene: Other: clinical trials; Roche: Other: clinical trials; Astellas: Other: clinical trials . Chatzidimitriou: Abbvie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding.

Defective Toll-Like Receptors Driven B Cell Response in Hyper IgE Syndrome Patients With STAT3 Mutations

Autosomal dominant hyper-IgE syndrome (AD-HIES) is a rare inherited primary immunodeficient disease (PIDs), which is caused by STAT3 gene mutations. Previous studies indicated a defective Toll-like receptor (TLR) 9-induced B cell response in AD-HIES patients, including proliferation, and IgG production. However, the other TLRs-mediated B cell responses in AD-HIES patients were not fully elucidated. In this study, we systematically studied the B cell response to TLRs signaling pathways in AD-HIES patients, including proliferation, activation, apoptosis, cytokine, and immunoglobulin production. Our results showed that the TLRs-induced B cell proliferation and activation was significantly impaired in AD-HIES patients. Besides, AD-HIES patients had defects in TLRs-induced B cell class switch, as well as IgG/IgM secretion and IL-10 production in B cells. Taken together, we first systematically reported the deficiency of TLRs driven B cell response in AD-HIES patients, which help to have a better understanding of the pathology of AD-HIES.

Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation

Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network (“DCNet”), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.

Multi-omic Analysis of Developing Human Retina and Organoids Reveals Cell-Specific Cis-Regulatory Elements and Mechanisms of Non-Coding Genetic Disease Risk.

Cis-regulatory elements (CREs) play a critical role in the development, maintenance, and disease-states of all human cell types. In the human retina, CREs have been implicated in a variety of inherited retinal disorders. To characterize cell-class-specific CREs in the human retina and elucidate their potential functions in development and disease, we performed single-nucleus (sn)ATAC-seq and snRNA-seq on the developing and adult human retina and on human retinal organoids. These analyses allowed us to identify cell-class-specific CREs, enriched transcription factor binding motifs, putative target genes, and to examine how these features change over development. By comparing DNA accessibility between the human retina and retinal organoids we found that CREs in organoids are highly correlated at the single-cell level, validating the use of organoids as a model for studying disease-associated CREs. As a proof of concept, we studied the function of a disease-associated CRE at 5q14.3 in organoids, identifying its principal target gene as the miR-9-2 primary transcript and demonstrating a dual role for this CRE in regulating neurogenesis and gene regulatory programs in mature glia. This study provides a rich resource for characterizing cell-class-specific CREs in the human retina and showcases retinal organoids as a model in which to study the function of retinal CREs that influence retinal development and disease.