Compartmentalization proteomics revealed endolysosomal protein network changes in a goat model of atrial fibrillation.

Endolysosomes (EL) are known for their role in regulating both intracellular trafficking and proteostasis. EL facilitate the elimination of damaged membranes, protein aggregates, membranous organelles and play an important role in calcium signaling. The specific role of EL in cardiac atrial fibrillation (AF) is not well understood. We isolated atrial EL organelles from AF goat biopsies and conducted a comprehensive integrated omics analysis to study the EL-specific proteins and pathways. We also performed electron tomography, protein and enzyme assays on these biopsies. Our results revealed the upregulation of the AMPK pathway and the expression of EL-specific proteins that were not found in whole tissue lysates, including GAA, DYNLRB1, CLTB, SIRT3, CCT2, and muscle-specific HSPB2. We also observed structural anomalies, such as autophagic-vacuole formation, irregularly shaped mitochondria, and glycogen deposition. Our results provide molecular information suggesting EL play a role in AF disease process over extended time frames.

Single nucleus and spatial transcriptomic profiling of healthy human hamstring tendon.

The molecular and cellular basis of health in human tendons remains poorly understood. Among human tendons, hamstring tendon has markedly low pathology and can provide a prototypic healthy tendon reference. The aim of this study was to determine the transcriptomes and location of all cell types in healthy hamstring tendon. Using single nucleus RNA sequencing, we profiled the transcriptomes of 10 533 nuclei from four healthy donors and identified 12 distinct cell types. We confirmed the presence of two fibroblast cell types, endothelial cells, mural cells, and immune cells, and identified cell types previously unreported in tendons, including different skeletal muscle cell types, satellite cells, adipocytes, and undefined nervous system cells. The location of these cell types within tendon was defined using spatial transcriptomics and imaging, and potential transcriptional networks and cell-cell interactions were analyzed. We demonstrate that fibroblasts have the highest number of potential cell-cell interactions in our dataset, are present throughout the tendon, and play an important role in the production and organization of extracellular matrix, thus confirming their role as key regulators of hamstring tendon homeostasis. Overall, our findings underscore the complexity of the cellular networks that underpin healthy human tendon function and the central role of fibroblasts as key regulators of hamstring tendon tissue homeostasis.

Iberdomide increases innate and adaptive immune cell subsets in the bone marrow of patients with relapsed/refractory multiple myeloma.

Iberdomide is a potent cereblon E3 ligase modulator (CELMoD agent) with promising efficacy and safety as a monotherapy or in combination with other therapies in patients with relapsed/refractory multiple myeloma (RRMM). Using a custom mass cytometry panel designed for large-scale immunophenotyping of the bone marrow tumor microenvironment (TME), we demonstrate significant increases of effector T and natural killer (NK) cells in a cohort of 93 patients with multiple myeloma (MM) treated with iberdomide, correlating findings to disease characteristics, prior therapy, and a peripheral blood immune phenotype. Notably, changes are dose dependent, associated with objective response, and independent of prior refractoriness to MM therapies. This suggests that iberdomide broadly induces innate and adaptive immune activation in the TME, contributing to its antitumor efficacy. Our approach establishes a strategy to study treatment-induced changes in the TME of patients with MM and, more broadly, patients with cancer and establishes rational combination strategies for iberdomide with immune-enhancing therapies to treat MM.

MicroRNA analysis of medium/large placenta extracellular vesicles in normal and preeclampsia pregnancies.

Background: Preeclampsia (PE) is a hypertensive disorder of pregnancy, affecting 2%-8% of pregnancies worldwide, and is the leading cause of adverse maternal and fetal outcomes. The disease is characterized by oxidative and cellular stress and widespread endothelial dysfunction. While the precise mechanisms are not entirely understood, the pathogenesis of PE is closely linked to placental dysfunction and, to some extent, syncytiotrophoblast extracellular vesicle release (STB-EVs). These vesicles can be divided into the less well-studied medium/large EVs (220-1,000 nm) released in response to stress and small EVs (<220 nm) released as a component of intercellular communication. The previously described production of m/lSTB-EVs in response to cellular stress combined with the overwhelming occurrence of cellular and oxidative stress in PE prompted us to evaluate the microRNAome of PE m/lSTB-EVs. We hypothesized that the microRNAome profile of m/lSTB-EVs is different in PE compared to normal pregnancy (NP), which might permit the identification of potential circulating biomarkers not previously described in PE. Methods/study design: We performed small RNA sequencing on medium/large STB-EVs isolated from PE and NP placentae using dual-lobe ex vivo perfusion. The sequencing data was bioinformatically analyzed to identify differentially regulated microRNAs. Identified microRNAs were validated with quantitative PCR analysis. We completed our analysis by performing an in-silico prediction of STB-EV mechanistic pathways. Results: We identified significant differences between PE and NP in the STB-EVs micro ribonucleic acid (microRNA) profiles. We verified the differential expression of hsa-miR-193b-5p, hsa-miR-324-5p, hsa-miR-652-3p, hsa-miR-3196, hsa-miR-9-5p, hsa-miR-421, and hsa-miR-210-3p in the medium/large STB-EVs. We also confirmed the differential abundance of hsa-miR-9-5p in maternal serum extracellular vesicles (S EVs). In addition, we integrated the results of these microRNAs into the previously published messenger RNA (mRNA) data to better understand the relationship between these biomolecules. Conclusions: We identified a differentially regulated micro-RNA, hsa-miR-9-5p, that may have biomarker potential and uncovered mechanistic pathways that may be important in the pathophysiology of PE.

Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries.

The advent of single-cell resolution sequencing and spatial transcriptomics has enabled the delivery of cellular and molecular atlases of tissues and organs, providing new insights into tissue health and disease. However, if the full potential of these technologies is to be equitably realised, ancestrally inclusivity is paramount. Such a goal requires greater inclusion of both researchers and donors in low- and middle-income countries (LMICs). In this perspective, we describe the current landscape of ancestral inclusivity in genomic and single-cell transcriptomic studies. We discuss the collaborative efforts needed to scale the barriers to establishing, expanding, and adopting single-cell sequencing research in LMICs and to enable globally impactful outcomes of these technologies.

Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules.

Unique molecular identifiers are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are a source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing unique molecular identifiers using homotrimeric nucleotide blocks provides an error-correcting solution that allows absolute counting of sequenced molecules.

PRMT5 inhibition shows in vitro efficacy against H3K27M-altered diffuse midline glioma, but does not extend survival in vivo.

H3K27-altered Diffuse Midline Glioma (DMG) is a universally fatal paediatric brainstem tumour. The prevalent driver mutation H3K27M creates a unique epigenetic landscape that may also establish therapeutic vulnerabilities to epigenetic inhibitors. However, while HDAC, EZH2 and BET inhibitors have proven somewhat effective in pre-clinical models, none have translated into clinical benefit due to either poor blood-brain barrier penetration, lack of efficacy or toxicity. Thus, there remains an urgent need for new DMG treatments. Here, we performed wider screening of an epigenetic inhibitor library and identified inhibitors of protein arginine methyltransferases (PRMTs) among the top hits reducing DMG cell viability. Two of the most effective inhibitors, LLY-283 and GSK591, were targeted against PRMT5 using distinct binding mechanisms and reduced the viability of a subset of DMG cells expressing wild-type TP53 and mutant ACVR1. RNA-sequencing and phenotypic analyses revealed that LLY-283 could reduce the viability, clonogenicity and invasion of DMG cells in vitro, representing three clinically important phenotypes, but failed to prolong survival in an orthotopic xenograft model. Together, these data show the challenges of DMG treatment and highlight PRMT5 inhibitors for consideration in future studies of combination treatments.

A cross-sectional analysis of syncytiotrophoblast membrane extracellular vesicles-derived transcriptomic biomarkers in early-onset preeclampsia.

Background: Preeclampsia (PE) is a pregnancy-specific hypertensive disorder affecting 2%-8% of pregnancies worldwide. Biomarker(s) for the disorder exists, but while these have excellent negative predictive value, their positive predictive value is poor. Extracellular vesicles released by the placenta into the maternal circulation, syncytiotrophoblast membrane extracellular vesicles (STB-EVs), have been identified as being involved in PE with the potential to act as liquid biopsies. Objective: The objective of this study was to identify the difference in the transcriptome of placenta and STB-EVs between preeclampsia and normal pregnancy (NP) and mechanistic pathways. Methods/study design: We performed RNA-sequencing on placental tissue, medium/large and small STB-EVs from PE (n = 6) and NP (n = 6), followed by bioinformatic analysis to identify targets that could be used in the future for EV-based diagnostic tests for preeclampsia. Some of the identified biomarkers were validated with real-time polymerase chain reactions. Results: Our analysis identified a difference in the transcriptomic STB-EV cargo between PE and NP. We then identified and verified the differential expression of FLNB, COL17A1, SLC45A4, LEP, HTRA4, PAPP-A2, EBI3, HSD17B1, FSTL3, INHBA, SIGLEC6, and CGB3. Our analysis also identified interesting mechanistic processes via an in silico prediction of STB-EV-based mechanistic pathways. Conclusions: In this study, using comprehensive profiling of differentially expressed/carried genes of three linked sample subtypes in PE, we identified potential biomarkers and mechanistic gene pathways that may be important in the pathophysiology of PE and could be further explored in future studies.

Epigenetic-focused CRISPR/Cas9 screen identifies (absent, small, or homeotic)2-like protein (ASH2L) as a regulator of glioblastoma cell survival.

BACKGROUND: Glioblastoma is the most common and aggressive primary brain tumor with extremely poor prognosis, highlighting an urgent need for developing novel treatment options. Identifying epigenetic vulnerabilities of cancer cells can provide excellent therapeutic intervention points for various types of cancers. METHOD: In this study, we investigated epigenetic regulators of glioblastoma cell survival through CRISPR/Cas9 based genetic ablation screens using a customized sgRNA library EpiDoKOL, which targets critical functional domains of chromatin modifiers. RESULTS: Screens conducted in multiple cell lines revealed ASH2L, a histone lysine methyltransferase complex subunit, as a major regulator of glioblastoma cell viability. ASH2L depletion led to cell cycle arrest and apoptosis. RNA sequencing and greenCUT&RUN together identified a set of cell cycle regulatory genes, such as TRA2B, BARD1, KIF20B, ARID4A and SMARCC1 that were downregulated upon ASH2L depletion. Mass spectrometry analysis revealed the interaction partners of ASH2L in glioblastoma cell lines as SET1/MLL family members including SETD1A, SETD1B, MLL1 and MLL2. We further showed that glioblastoma cells had a differential dependency on expression of SET1/MLL family members for survival. The growth of ASH2L-depleted glioblastoma cells was markedly slower than controls in orthotopic in vivo models. TCGA analysis showed high ASH2L expression in glioblastoma compared to low grade gliomas and immunohistochemical analysis revealed significant ASH2L expression in glioblastoma tissues, attesting to its clinical relevance. Therefore, high throughput, robust and affordable screens with focused libraries, such as EpiDoKOL, holds great promise to enable rapid discovery of novel epigenetic regulators of cancer cell survival, such as ASH2L. CONCLUSION: Together, we suggest that targeting ASH2L could serve as a new therapeutic opportunity for glioblastoma. Video Abstract.

Mass cytometry as a tool in target validation and drug discovery.

Mass cytometry provides highly multiparametric data at a single cell level, coupling the specificity and sensitivity of time-of-flight mass spectrometry with the single-cell throughput of flow cytometry. It offers great value in interrogating the potentially heterogenous impact that a drug may have on a biological system, allowing an investigator to capture not just changes in cell behavior, but how these changes may differ between cell subtypes. In this chapter, we review the technical details of the platform as well as its limitations, before describing our approach to planning and running a mass cytometry experiment. A series of method modules, spanning the staining process through to data cleaning, are described that are then combined to create three separate experiments. The first experiment illustrates a core process in mass cytometry: the validation and titration of a metal-conjugated antibody reporter. The second experiment explores the impact of a kinase inhibitor on cell cycle and apoptosis pathways of a human myeloma cell line. And the third experiment exploits the multiparametric capability of mass cytometry, by exploring the differential expression changes in a transcription factor upon drug treatment across the cellular compartments of a peripheral blood mononuclear cell sample.

A roadmap for delivering a human musculoskeletal cell atlas.

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Phenotypic Chemical Screening in CD4+ T Cells to Identify Epigenetic Inhibitors.

Chemical biology provides an attractive approach to identify genes involved in a particular biological process. This screening approach has its advantages because the assays are usually non-destructive, and analysis can be performed even if the mechanism of action is unknown. During an immune reaction, cells upregulate the expression and secretion of small proteins called cytokines that have specific effects on the interactions and communication between cells. Here, we describe the principles and steps involved in the execution of chemical screening for identifying epigenetic inhibitors that affect cytokine production in differentiated Th1, Th2, and Th17 cells. Our approach provides a rationale for identifying epigenetic chemical compounds that are capable of controlling CD4+ T-cell cytokine function that may be beneficial for treating inflammatory diseases.

Uptake of long-chain fatty acids from the bone marrow suppresses CD8+ T-cell metabolism and function in multiple myeloma.

T cells demonstrate impaired function in multiple myeloma (MM) but suppressive mechanisms in the bone marrow microenvironment remain poorly defined. We observe that bone marrow CD8+ T-cell function is decreased in MM compared with controls, and is also consistently lower within bone marrow samples than in matched peripheral blood samples. These changes are accompanied by decreased mitochondrial mass and markedly elevated long-chain fatty acid uptake. In vitro modeling confirmed that uptake of bone marrow lipids suppresses CD8+ T function, which is impaired in autologous bone marrow plasma but rescued by lipid removal. Analysis of single-cell RNA-sequencing data identified expression of fatty acid transport protein 1 (FATP1) in bone marrow CD8+ T cells in MM, and FATP1 blockade also rescued CD8+ T-cell function, thereby identifying this as a novel target to augment T-cell activity in MM. Finally, analysis of samples from cohorts of patients who had received treatment identified that CD8+ T-cell metabolic dysfunction resolves in patients with MM who are responsive to treatment but not in patients with relapsed MM, and is associated with substantial T-cell functional restoration.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Mature primary human osteocytes in mini organotypic cultures secrete FGF23 and PTH1-34-regulated sclerostin.

Introduction: For decades, functional primary human osteocyte cultures have been crucially needed for understanding their role in bone anabolic processes and in endocrine phosphate regulation via the bone-kidney axis. Mature osteocyte proteins (sclerostin, DMP1, Phex and FGF23) play a key role in various systemic diseases and are targeted by successful bone anabolic drugs (anti-sclerostin antibody and teriparatide (PTH1-34)). However, cell lines available to study osteocytes produce very little sclerostin and low levels of mature osteocyte markers. We have developed a primary human 3D organotypic culture system that replicates the formation of mature osteocytes in bone. Methods: Primary human osteoblasts were seeded in a fibrinogen / thrombin gel around 3D-printed hanging posts. Following contraction of the gel around the posts, cells were cultured in osteogenic media and conditioned media was collected for analysis of secreted markers of osteocyte formation. Results: The organoids were viable for at least 6 months, allowing co-culture with different cell types and testing of bone anabolic drugs. Bulk RNAseq data displayed the developing marker trajectory of ossification and human primary osteocyte formation in vitro over an initial 8- week period. Vitamin D3 supplementation increased mineralization and sclerostin secretion, while hypoxia and PTH1-34 modulated sclerostin. Our culture system also secreted FGF23, enabling the future development of a bone-kidney-parathyroid-vascular multi-organoid or organ-on-a-chip system to study disease processes and drug effects using purely human cells. Discussion: This 3D organotypic culture system provides a stable, long-lived, and regulated population of mature human primary osteocytes for a variety of research applications.

Long-Read Single-Cell Sequencing Using scCOLOR-seq.

Single-cell sequencing allows for the measurement of sequence information from individual cells with next-generation sequencing (NGS). However, its application to third-generation sequencing platforms such as Oxford Nanopore has been challenging because of its lower basecalling accuracy. Here we describe the method to perform highly accurate single-cell COrrected Long-Read sequencing (scCOLOR-seq) by droplet-based encapsulation of cells and sequencing using the Oxford Nanopore Sequencing system.

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.