Universal prediction of cell-cycle position using transfer learning

Insights into the role of the N6-methyladenosine reader IGF2BP3 in the progression of oral squamous cell carcinoma and its connection to cell-cycle control

The genome of oral squamous cell carcinoma (OSCC) has been extensively characterized via bulk sequencing, revealing a multitude of genetic changes. The gene IGF2BP3, which encodes for the insulin-like growth factor 2 mRNA-binding protein 3, has been observed to be highly expressed in several types of cancer. This finding suggests that IGF2BP3 may play a significant role in the initiation and advancement of cancer. Nevertheless, the mechanisms by which IGF2BP3 contribute to OSCC are yet to be fully understood. In this study, we have observed that IGF2BP3 exhibits overexpression in OSCC. Based on our findings from bulk sequencing analysis, we have concluded that IGF2BP3 could potentially serve as a biomarker for predicting poor prognosis in OSCC. Moreover, it has been demonstrated that IGF2BP3 exhibits a significant association with the initiation and advancement of tumors both in vivo and in vitro. The evaluation of IGF2BP3 expression levels in relation to the cell cycle stage was conducted using single-cell RNA sequencing data. Tumor cells characterized by elevated IGF2BP3 expression demonstrated a higher percentage of cells in the G2/M transition phase. This study presents new findings indicating that the molecular target IGF2BP3 can serve as a prognostic indicator for tumors and has an impact on the development and progression of OSCC by influencing the regulation of the cell cycle.

Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg

Endothelial cells are a heterogeneous population with various organ-specific and conserved functions that are critical to organ development, function, and regeneration. Here we report a Sox17-Erg direct reprogramming approach that uses cardiac fibroblasts to create differentiated endothelial cells that demonstrate endothelial-like molecular and physiological functions in vitro and in vivo. Injection of these induced endothelial cells into myocardial infarct sites after injury results in improved vascular perfusion of the scar region. Furthermore, we use genomic analyses to illustrate that Sox17-Erg reprogramming instructs cardiac fibroblasts toward an arterial-like identity. This results in a more efficient direct conversion of fibroblasts into endothelial-like cells when compared to traditional Etv2-based reprogramming. Overall, this Sox17-Erg direct reprogramming strategy offers a robust tool to generate endothelial cells both in vitro and in vivo, and has the potential to be used in repairing injured tissue.

Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle

Glioblastoma (GBM) is characterized by significant heterogeneity, leading to poor survival outcomes for patients, despite the implementation of comprehensive treatment strategies. The roles of cyclin A2 (CCNA2) and NIMA related kinase 2 (NEK2) have been extensively studied in numerous cancers, but their specific functions in GBM remain to be elucidated. The present study aimed to investigate the potential molecular mechanisms of CCNA2 and NEK2 in GBM. CCNA2 and NEK2 expression and prognosis in glioma were evaluated by bioinformatics methods. In addition, the distribution of CCNA2 and NEK2 expression in GBM subsets was determined using pseudo-time analysis and tricycle position of single-cell sequencing. Gene Expression Omnibus and Kyoto Encyclopedia of Genes and Genome databases were employed and enrichment analyses were conducted to investigate potential signaling pathways in GBM subsets and a nomogram was established to predict 1-, 2- and 3-year overall survival probability in GBM. CCNA2 and NEK2 expression levels were further validated by western blot analysis and immunohistochemical staining in GBM samples. High expression of CCNA2 and NEK2 in glioma indicates poor clinical outcomes. Single-cell sequencing of GBM revealed that these genes were upregulated in a subset of positive neural progenitor cells (P-NPCs), which showed significant proliferation and progression properties and may activate G2M checkpoint pathways. A comprehensive nomogram predicts 1-, 2- and 3-year overall survival probability in GBM by considering P-NPCs, age, chemotherapy and radiotherapy scores. CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle, thus indicating the potential of novel therapy directed to CCNA2 and NEK2 in GBM.

Breaking out of the cycle: Including quiescence in cell cycle classification

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state, and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat (https://github.com/plaisier-lab/ccafv2_R) or a PyPI package integrated with scanpy (https://pypi.org/project/ccAFv2/). We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment.

METHODS

Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312).

RESULTS

Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05).

CONCLUSION

This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

Short cell cycle duration is a phenotype of human epidermal stem cells

BACKGROUND

A traditional view is that stem cells (SCs) divide slowly. Meanwhile, both embryonic and pluripotent SCs display a shorter cell cycle duration (CCD) in comparison to more committed progenitors (CPs).

METHODS

We examined the in vitro proliferation and cycling behavior of somatic adult human cells using live cell imaging of passage zero keratinocytes and single-cell RNA sequencing.

RESULTS

We found two populations of keratinocytes: those with short CCD and protracted near exponential growth, and those with long CCD and terminal differentiation. Applying the ergodic principle, the comparative numbers of cycling cells in S phase in an enriched population of SCs confirmed a shorter CCD than CPs. Further, analysis of single-cell RNA sequencing of cycling adult human keratinocyte SCs and CPs indicated a shortening of both G1 and G2M phases in the SC.

CONCLUSIONS

Contrary to the pervasive paradigm, SCs progress through cell cycle more quickly than more differentiated dividing CPs. Thus, somatic human adult keratinocyte SCs may divide infrequently, but divide rapidly when they divide. Additionally, it was found that SC-like proliferation persisted in vitro.

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo

Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.

From G1 to M: a comparative study of methods for identifying cell cycle phases

Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

Unraveling the underlying pathogenic factors driving nonalcoholic steatohepatitis and hepatocellular carcinoma: an in-depth analysis of prognostically relevant gene signatures in hepatocellular carcinoma

BACKGROUND

Nonalcoholic steatohepatitis (NASH) is a progressive manifestation of nonalcoholic fatty liver disease (NAFLD) that can lead to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Despite the growing knowledge of NASH and HCC, the association between the two conditions remains to be fully explored. Bioinformatics has emerged as a valuable approach for identifying disease-specific feature genes, enabling advancements in disease prediction, prevention, and personalized treatment strategies.

MATERIALS AND METHODS

In this study, we utilized CellChat, copy number karyotyping of aneuploid tumors (CopyKAT), consensus Non-negative Matrix factorization (cNMF), Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), Monocle, spatial co-localization, single sample gene set enrichment analysis (ssGSEA), Slingshot, and the Scissor algorithm to analyze the cellular and immune landscape of NASH and HCC. Through the Scissor algorithm, we identified three cell types correlating with disease phenotypic features and subsequently developed a novel clinical prediction model using univariate, LASSO, and multifactor Cox regression.

RESULTS

Our results revealed that macrophages are a significant pathological factor in the development of NASH and HCC and that the macrophage migration inhibitory factor (MIF) signaling pathway plays a crucial role in cellular crosstalk at the molecular level. We deduced three prognostic genes (YBX1, MED8, and KPNA2), demonstrating a strong diagnostic capability in both NASH and HCC.

CONCLUSION

These findings shed light on the pathological mechanisms shared between NASH and HCC, providing valuable insights for the development of novel clinical strategies.

The unfolded protein response pathway as a possible link in the pathogenesis of COVID-19 and sepsis

The global impact of the COVID-19 pandemic has been substantial. Emerging evidence underscores a strong clinical connection between COVID-19 and sepsis. Numerous studies have identified the unfolded protein response (UPR) pathway as a crucial pathogenic pathway for both COVID-19 and sepsis, but it remains to be investigated whether this signaling pathway operates as a common pathogenic mechanism for both COVID-19 and sepsis. In this study, single-cell RNA-seq data and transcriptome data for COVID-19 and sepsis cases were downloaded from GEO (Gene Expression Omnibus). By analyzing the single-cell transcriptome data, we identified B cells as the critical cell subset and the UPR pathway as the critical signaling pathway. Based on the transcriptome data, a machine learning diagnostic model was then constructed using the interleaved genes of B-cell-related and UPR-pathway-related genes. We validated the diagnostic model using both internal and external datasets and found the accuracy and stability of this model to be extremely strong. Even after integrating our algorithmic model with the patient's clinical status, it continued to yield identical results, further emphasizing the reliability of this model. This study provides a novel molecular perspective on the pathogenesis of sepsis and COVID-19 at the single-cell level and suggests that these two diseases may share a common mechanism.

Age-associated changes in lineage composition of the enteric nervous system regulate gut health and disease

The enteric nervous system (ENS), a collection of neural cells contained in the wall of the gut, is of fundamental importance to gastrointestinal and systemic health. According to the prevailing paradigm, the ENS arises from progenitor cells migrating from the neural crest and remains largely unchanged thereafter. Here, we show that the lineage composition of maturing ENS changes with time, with a decline in the canonical lineage of neural-crest derived neurons and their replacement by a newly identified lineage of mesoderm-derived neurons. Single cell transcriptomics and immunochemical approaches establish a distinct expression profile of mesoderm-derived neurons. The dynamic balance between the proportions of neurons from these two different lineages in the post-natal gut is dependent on the availability of their respective trophic signals, GDNF-RET and HGF-MET. With increasing age, the mesoderm-derived neurons become the dominant form of neurons in the ENS, a change associated with significant functional effects on intestinal motility which can be reversed by GDNF supplementation. Transcriptomic analyses of human gut tissues show reduced GDNF-RET signaling in patients with intestinal dysmotility which is associated with reduction in neural crest-derived neuronal markers and concomitant increase in transcriptional patterns specific to mesoderm-derived neurons. Normal intestinal function in the adult gastrointestinal tract therefore appears to require an optimal balance between these two distinct lineages within the ENS.

Histone demethylase KDM4B contributes to advanced clear cell renal carcinoma and association with copy number variations and cell cycle progression

Advanced renal cell carcinoma (RCC) poses a threat to patient survival. Epigenetic remodelling is the pathogenesis of renal cancer. Histone demethylase 4B (KDM4B) is overexpressed in many cancers through various pathways. However, the role of KDM4B in clear cell renal carcinoma has not yet been elucidated. The differential expression of KDM4B was first verified by analysing public databases. The expression of KDM4B in fresh tissues and pathology slides was further analysed by western blotting and immunohistochemical staining. KDM4B overexpression and knockdown cell lines were also established. Cell Counting Kit-8 (CCK-8) assay was used to detect cell growth. Transwell assays were performed to assess cell migration. Xenografts were used to evaluate tumour growth and metastasis in vivo. Finally, KDM4B expression levels associated with copy number variation (CNV) and cell cycle stage were evaluated based on single-cell RNA sequencing data. KDM4B was expressed at higher levels in tumour tissues than in the adjacent normal tissues. High levels of KDM4B are associated with worse pathological features and poorer prognosis. KDM4B also promotes cell proliferation and migration in vitro, as well as tumour growth and metastasis in vivo. Tumour cells with high KDM4B expression exhibited higher CNV levels and a greater proportion of cells in the G1/S transition phase. Our results confirm that KDM4B promotes the progression of clear cell renal carcinoma, is correlated with poor prognosis, and may be related to high levels of CNV and cell cycle progression.

Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates

BACKGROUND

RNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflects known features of the system. However, the limitations of RNA velocity estimates are still not well understood.

RESULTS

We analyze the impact of different steps in the RNA velocity workflow on direction and speed. We consider both high-dimensional velocity estimates and low-dimensional velocity vector fields mapped onto an embedding. We conclude the transition probability method for mapping velocity estimates onto an embedding is effectively interpolating in the embedding space. Our findings reveal a significant dependence of the RNA velocity workflow on smoothing via the k-nearest-neighbors (k-NN) graph of the observed data. This reliance results in considerable estimation errors for both direction and speed in both high- and low-dimensional settings when the k-NN graph fails to accurately represent the true data structure; this is an unknown feature of real data. RNA velocity performs poorly at estimating speed in both low- and high-dimensional spaces, except in very low noise settings. We introduce a novel quality measure that can identify when RNA velocity should not be used.

CONCLUSIONS

Our findings emphasize the importance of choices in the RNA velocity workflow and highlight critical limitations of data analysis. We advise against over-interpreting expression dynamics using RNA velocity, particularly in terms of speed. Finally, we emphasize that the use of RNA velocity in assessing the correctness of a low-dimensional embedding is circular.

Complete miRNA-15/16 loss in mice promotes hematopoietic progenitor expansion and a myeloid-biased hyperproliferative state

Dysregulated apoptosis and proliferation are fundamental properties of cancer, and microRNAs (miRNA) are critical regulators of these processes. Loss of miR-15a/16-1 at chromosome 13q14 is the most common genomic aberration in chronic lymphocytic leukemia (CLL). Correspondingly, the deletion of either murine miR-15a/16-1 or miR-15b/16-2 locus in mice is linked to B cell lymphoproliferative malignancies. However, unexpectedly, when both miR-15/16 clusters are eliminated, most double knockout (DKO) mice develop acute myeloid leukemia (AML). Moreover, in patients with CLL, significantly reduced expression of miR-15a, miR-15b, and miR-16 associates with progression of myelodysplastic syndrome to AML, as well as blast crisis in chronic myeloid leukemia. Thus, the miR-15/16 clusters have a biological relevance for myeloid neoplasms. Here, we demonstrate that the myeloproliferative phenotype in DKO mice correlates with an increase of hematopoietic stem and progenitor cells (HSPC) early in life. Using single-cell transcriptomic analyses, we presented the molecular underpinning of increased myeloid output in the HSPC of DKO mice with gene signatures suggestive of dysregulated hematopoiesis, metabolic activities, and cell cycle stages. Functionally, we found that multipotent progenitors (MPP) of DKO mice have increased self-renewing capacities and give rise to significantly more progeny in the granulocytic compartment. Moreover, a unique transcriptomic signature of DKO MPP correlates with poor outcome in patients with AML. Together, these data point to a unique regulatory role for miR-15/16 during the early stages of hematopoiesis and to a potentially useful biomarker for the pathogenesis of myeloid neoplasms.

Ret deficiency decreases neural crest progenitor proliferation and restricts fate potential during enteric nervous system development

The receptor tyrosine kinase RET plays a critical role in the fate specification of enteric neural crest-derived cells (ENCDCs) during enteric nervous system (ENS) development. RET loss of function (LoF) is associated with Hirschsprung disease (HSCR), which is marked by aganglionosis of the gastrointestinal (GI) tract. Although the major phenotypic consequences and the underlying transcriptional changes from Ret LoF in the developing ENS have been described, cell type- and state-specific effects are unknown. We performed single-cell RNA sequencing on an enriched population of ENCDCs from the developing GI tract of Ret null heterozygous and homozygous mice at embryonic day (E)12.5 and E14.5. We demonstrate four significant findings: 1) Ret-expressing ENCDCs are a heterogeneous population comprising ENS progenitors as well as glial- and neuronal-committed cells; 2) neurons committed to a predominantly inhibitory motor neuron developmental trajectory are not produced under Ret LoF, leaving behind a mostly excitatory motor neuron developmental program; 3) expression patterns of HSCR-associated and Ret gene regulatory network genes are impacted by Ret LoF; and 4) Ret deficiency leads to precocious differentiation and reduction in the number of proliferating ENS precursors. Our results support a model in which Ret contributes to multiple distinct cellular phenotypes during development of the ENS, including the specification of inhibitory neuron subtypes, cell cycle dynamics of ENS progenitors, and the developmental timing of neuronal and glial commitment.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

Background

Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer.

Methods

The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the "irGSEA" R package. The estimation of cell cycle was conducted with the "tricycle" R package, and intercellular communication networks were analyzed using "CellChat". For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized.

Results

Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression.

Conclusion

The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Multi-Omic Analysis of CIC's Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity

CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC's interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3' and 5' untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC's functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC's potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts.

Chemotherapy-induced tumor immunogenicity is mediated in part by megakaryocyte-erythroid progenitors

Chemotherapy remains one of the main treatment modalities for cancer. While chemotherapy is mainly known for its ability to kill tumor cells directly, accumulating evidence indicates that it also acts indirectly by enhancing T cell-mediated anti-tumor immunity sometimes through immunogenic cell death. However, the role of immature immune cells in chemotherapy-induced immunomodulation has not been studied. Here, we utilized a mouse pancreatic cancer model to characterize the effects of gemcitabine chemotherapy on immature bone marrow cells in the context of tumor immunogenicity. Single cell RNA sequencing of hematopoietic stem and progenitor cells revealed a 3-fold increase in megakaryocyte-erythroid progenitors (MEPs) in the bone marrow of gemcitabine-treated mice in comparison to untreated control mice. Notably, adoptive transfer of MEPs to pancreatic tumor-bearing mice significantly reduced tumor growth and increased the levels of anti-tumor immune cells in tumors and peripheral blood. Furthermore, MEPs increased the tumor cell killing activity of CD8 + T cells and NK cells, an effect that was dependent on MEP-secreted CCL5 and CXCL16. Collectively, our findings demonstrate that chemotherapy-induced enrichment of MEPs in the bone marrow compartment contributes to anti-tumor immunity.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Entinostat decreases immune suppression to promote anti-tumor responses in a HER2+ breast tumor microenvironment

Therapeutic combinations to alter immunosuppressive, solid tumor microenvironments (TMEs), such as in breast cancer, are essential to improve responses to immune checkpoint inhibitors (ICIs). Entinostat, an oral histone deacetylase inhibitor (HDACi), has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs. The precise and comprehensive alterations to the TME induced by entinostat remain unknown. Here, we employed single-cell RNA-sequencing on HER2-overexpressing breast tumors from mice treated with entinostat and ICIs in order to fully characterize changes across multiple cell types within the TME. This analysis demonstrates that treatment with entinostat induced a shift from a pro-tumor to an anti-tumor TME signature, characterized predominantly by changes in myeloid cells. We confirmed myeloid-derived suppressor cells (MDSCs) within entinostat-treated tumors associated with a less suppressive granulocytic (G)-MDSC phenotype and exhibited altered suppressive signaling that involved the NFkB and STAT3 pathways. In addition to MDSCs, tumor-associated macrophages were epigenetically reprogrammed from a pro-tumor M2-like phenotype toward an anti-tumor M1-like phenotype, which may be contributing to a more sensitized TME. Overall, our in-depth analysis suggests that entinostat-induced changes on multiple myeloid cell types reduce immunosuppression and increase anti-tumor responses, which, in turn, improve sensitivity to ICIs. Sensitization of the TME by entinostat could ultimately broaden the population of patients with breast cancer who could benefit from ICIs.