Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in ...principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.
To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.
Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.
Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are ...being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.
Ulcerative colitis and Crohn's disease are chronic inflammatory intestinal diseases with perplexing heterogeneity in disease manifestation and response to treatment. While the molecular basis for ...this heterogeneity remains uncharacterized, single-cell technologies allow us to explore the transcriptional states within tissues at an unprecedented resolution which could further understanding of these complex diseases. Here, we apply single-cell RNA-sequencing to human inflamed intestine and show that the largest differences among patients are present within the myeloid compartment including macrophages and neutrophils. Using spatial transcriptomics in human tissue at single-cell resolution (CosMx Spatial Molecular Imaging) we spatially localize each of the macrophage and neutrophil subsets identified by single-cell RNA-sequencing and unravel further macrophage diversity based on their tissue localization. Finally, single-cell RNA-sequencing combined with single-cell spatial analysis reveals a strong communication network involving macrophages and inflammatory fibroblasts. Our data sheds light on the cellular complexity of these diseases and points towards the myeloid and stromal compartments as important cellular subsets for understanding patient-to-patient heterogeneity.
Colorectal cancers (CRCs) are composed of an amalgam of cells with distinct genotypes and phenotypes. Here, we reveal a previously unappreciated heterogeneity in the biosynthetic capacities of CRC ...cells. We discover that the majority of ribosomal DNA transcription and protein synthesis in CRCs occurs in a limited subset of tumor cells that localize in defined niches. The rest of the tumor cells undergo an irreversible loss of their biosynthetic capacities as a consequence of differentiation. Cancer cells within the biosynthetic domains are characterized by elevated levels of the RNA polymerase I subunit A (POLR1A). Genetic ablation of POLR1A-high cell population imposes an irreversible growth arrest on CRCs. We show that elevated biosynthesis defines stemness in both LGR5+ and LGR5− tumor cells. Therefore, a common architecture in CRCs is a simple cell hierarchy based on the differential capacity to transcribe ribosomal DNA and synthesize proteins.
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•rRNA and proteins in CRCs are produced in dedicated tumor domains•Differentiated tumor cells experience an irreversible loss of biosynthetic capacities•POLR1A-high CRC cells reside at the top of the tumor cell hierarchy•Both LGR5+ and LGR5− tumor cells within biosynthetic niches fuel tumor growth
Morral and colleagues discovered that most rRNA and proteins synthesized in colorectal cancers (CRCs) are contributed by a limited subset of tumor cells that reside adjacent to the stroma. This architecture defines a common stem cell hierarchy. In some CRCs, the biosynthetic tumor cell population renders LGR5+ tumor cells dispensable.
Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. ...Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (
)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.
Proteasome inhibitors (PI) are extensively used for the therapy of multiple myeloma (MM) and mantle cell lymphoma. However, patients continuously relapse or are intrinsically resistant to this class ...of drugs. Here, to identify targets that synergize with PI, we carried out a functional screening in MM cell lines using a short hairpin RNA library against cancer driver genes. Isocitrate dehydrogenase 2 (IDH2) was identified as a top candidate, showing a synthetic lethal activity with the PI carfilzomib (CFZ). Combinations of US Food and Drug Administration–approved PI with a pharmacological IDH2 inhibitor (AGI-6780) triggered synergistic cytotoxicity in MM, mantle cell lymphoma, and Burkitt lymphoma cell lines. CFZ/AGI-6780 treatment increased death of primary CD138+ cells from MM patients and exhibited a favorable cytotoxicity profile toward peripheral blood mononuclear cells and bone marrow–derived stromal cells. Mechanistically, the CFZ/AGI-6780 combination significantly decreased tricarboxylic acid cycle activity and adenosine triphosphate levels as a consequence of enhanced IDH2 enzymatic inhibition. Specifically, CFZ treatment reduced the expression of nicotinamide phosphoribosyltransferase (NAMPT), thus limiting IDH2 activation through the NAD+-dependent deacetylase SIRT3. Consistently, combination of CFZ with either NAMPT or SIRT3 inhibitors impaired IDH2 activity and increased MM cell death. Finally, inducible IDH2 knockdown enhanced the therapeutic efficacy of CFZ in a subcutaneous xenograft model of MM, resulting in inhibition of tumor progression and extended survival. Taken together, these findings indicate that NAMPT/SIRT3/IDH2 pathway inhibition enhances the therapeutic efficacy of PI, thus providing compelling evidence for treatments with lower and less toxic doses and broadening the application of PI to other malignancies.
•IDH2 is a new synthetic lethal target to PI, efficacious in several hematological malignancies.•Inhibition of NAMPT/SIRT3/IDH2 pathway could enhance the therapeutic efficacy and overcome resistance to PI.
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We report on the green preparation of one-dimensional metal coordination polymers by sonochemical approach. The spacer ligand 4,4′-bipyridine was ultrasonicated with chloride or acetate zinc salts to ...obtain Zn(4,4′-bipy)Cl2∞ and Zn(4,4′-bipy)2(OAc)2∞, respectively. Benign solvents such as ethanol and water were selected as reaction media, and the synthesis took place in a few minutes—a very short time compared to conventional methods where some days’ synthesis is required. X-ray powder diffraction, Fourier transform infrared spectroscopy, thermal analysis (thermogravimetric and differential scanning calorimetry), and CHN techniques investigated the influence of using different reaction solvents on the chemical, structural, and thermal properties of the final products. The 1D Zn(4,4′-bipy)Cl2∞ and Zn(4,4′-bipy)2(OAc)2∞ polymers, in agreement with the structures reported in the literature, were obtained in the form of nanocrystals with an average crystal size around 100 nm. As a proof of concept, a set of Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Klebsiella pneumoniae), and three yeast strains (Candida albicans, Candida krusei, Candida glabrata) were tested to evaluate the antimicrobial activity of the coordination polymers, following the Kirby–Bauer procedure and microplate dilution method. Thus, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and minimal biofilm inhibitory concentration (MBIC) were evaluated. Except for Candida krusei, the compounds showed an appreciable antimicrobial and antibiofilm activity against these strains grown in the liquid medium.
Anaplastic large-cell lymphoma (ALCL) is a clinical and biological heterogeneous disease that includes systemic anaplastic lymphoma kinase (ALK)-positive and ALK-negative entities. To discover ...biomarkers and/or genes involved in ALK-negative ALCL pathogenesis, we applied the cancer outlier profile analysis algorithm to a gene expression profiling data set including 249 cases of T-cell non-Hodgkin lymphoma and normal T cells. Ectopic coexpression of ERBB4 and COL29A1 genes was detected in 24% of ALK-negative ALCL patients. RNA sequencing and 5′ RNA ligase-mediated rapid amplification of complementary DNA ends identified 2 novel ERBB4-truncated transcripts displaying intronic transcription start sites. By luciferase assays, we defined that the expression of ERBB4-aberrant transcripts is promoted by endogenous intronic long terminal repeats. ERBB4 expression was confirmed at the protein level by western blot analysis and immunohistochemistry. Lastly, we demonstrated that ERBB4-truncated forms show oncogenic potentials and that ERBB4 pharmacologic inhibition partially controls ALCL cell growth and disease progression in an ERBB4-positive patient-derived tumorgraft model. In conclusion, we identified a new subclass of ALK-negative ALCL characterized by aberrant expression of ERBB4-truncated transcripts carrying intronic 5′ untranslated regions.
•Endogenous intronic long terminal repeats promote the ectopic expression of truncated ERBB4 transcripts in 24% of ALK-negative ALCL.•The expression of ERBB4-aberrant transcripts defines a new subclass of ALK-negative ALCL and may contribute to ALCL transformation.
Multiple myeloma (MM) is a dreadful disease, marked by the uncontrolled proliferation of clonal plasma cells (PCs) within the bone marrow (BM). MM is characterized by a highly heterogeneous clinical ...and molecular background, supported by severe genomic alterations. Important deregulation of long non-coding RNAs (lncRNAs) expression has been reported in MM patients, influencing progression and therapy resistance. NEAT1 is a lncRNA essential for nuclear paraspeckles and involved in gene expression regulation. We showed that NEAT1 supports MM proliferation making this lncRNA an attractive therapeutic candidate. Here, we used a combinatorial strategy integrating transcriptomic and computational approaches with functional high-throughput drug screening, to identify compounds that synergize with NEAT1 inhibition in restraining MM cells growth. AUKA inhibitors were identified as top-scoring drugs in these analyses. We showed that the combination of NEAT1 silencing and AURKA inhibitors in MM profoundly impairs microtubule organization and mitotic spindle assembly, finally leading to cell death. Analysis of the large publicly CoMMpass dataset showed that in MM patients AURKA expression is strongly associated with reduced progression-free (p < 0.0001) and overall survival probability (p < 0.0001) and patients displaying high expression levels of both NEAT1 and AURKA have a worse clinical outcome. Finally, using RNA-sequencing data from NEAT1 knockdown (KD) MM cells, we identified the AURKA allosteric regulator TPX2 as a new NEAT1 target in MM and as a mediator of the interplay between AURKA and NEAT1, therefore providing a possible explanation of the synergistic activity observed upon their combinatorial inhibition.
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