Multi-omics Technologies Research Articles

Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics

Introduction: Pediatric acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by various chromosomal abnormalities and somatic mutations. Investigating the origin of tumorigenesis and deciphering the complex interplay between AML cells and their surrounding tumor immune microenvironment (TIME) is crucial to understand the underlying mechanisms driving disease progression and response to therapy. Recent advancements in single-cell multi-omics technologies enabled us to characterize the transcriptional (GEX) and epigenetic (ATAC) landscapes of both tumors and TIMEs. Leveraging our in-house algorithm, scMINER (single-cell Mutual Information-based Network Engineering Ranger, Ding et al., 2023), this study aims to build a comprehensive cell type atlas in diverse pediatric AML bone marrow samples and construct intra- and inter-cellular networks, enabling the identification of hidden drivers that shape subtype-specific AML-TIME interactions. Methods: Using CD45 and CD33 as selective markers by flow cytometry, we have successfully isolated blasts and enriched for T cell and B cell populations for 10X single-cell multiome profiling across AML molecular subtypes, including UBTF-TD (n=1), NPM1 (n=1), and KMT2A rearrangement (n=1). We interrogated the GEX data using scMINER, including two key steps: (i) mutual information-based clustering analysis (MICA) and (ii) mutual information-based network inference engine (MINIE). MICA characterizes the intrinsic nonlinear similarity of gene expression distributions among cells, enabling a high resolution of clusters. MINIE reverse-engineers cluster-specific intracellular gene networks and infers protein activity of transcription factors (TF) or signaling factors (SIG) drivers based on the expression of its predicted regulon targets in the corresponding cluster. The activity profiles overcome the dropout effects of single-cell RNA-seq data and reflect protein activities across all cells. ArchR is used to analyze ATAC data. CellChat is used to construct and evaluate intercellular networks. Results: AML cells from three samples were divided into four major clusters, and we labeled them as NPM1, KMT2A, UBTF_one, and UBTF_two based on sample subtypes (Fig.1A). MICA managed to obtain distinct AML cell sub-clusters. Comparison with single-cell profiles of normal hematopoietic cells and annotation with marker genes revealed their correspondence with different myeloid developmental stages. Notably, the KMT2A cluster shared a high similarity in cell type proportion with the UBTF_two cluster, and the NPM1 cluster is closer to the UBTF_one cluster. This result demonstrated significant tumor heterogeneity within and across AML molecular subtypes. With the help of intracellular gene networks generated by MINIE, we identified multiple subtype-specific hidden drivers by differential activity analysis. For example, KMT2A and UBTF_two clusters exhibited elevated SAMHD1 levels, consistent with previous GSE data (Zhang et al., 2022). Chromatin accessibility analysis of ATAC data showed increased peaks near SAMHD1 in KMT2A and UBTF_two clusters, further supporting this observation. IRF8 showed a similar expression pattern to SAMHD1, suggesting its context-dependent role across AML subtypes. Accurate annotation of myeloid and immune cells greatly enriches the preciseness of the AML and TIME interaction patterns, which vary across AML molecular subtypes (Fig.1B). Interestingly, cells originated from KMT2A cluster exhibited similar interaction patterns to UBTF_two, while NPM1 and UBTF_one were grouped. This result indicated that different AML molecular subtypes may partially utilize the same mechanisms of tumor-TIME interaction, resulting in similar tumor phenotypes. Conclusions: Accurate clustering and well-defined annotation of myeloid and immune cells enabled us to build a comprehensive cell type atlas in AML samples. The subtype-specific network and the following analysis identified candidate hidden drivers that possibly contribute to the heterogeneity of tumor populations. AML subtypes sharing the same hidden drivers have similar cell type proportions and interaction patterns with TIMEs. This observation implies that AML subtypes share cell type-specific dependencies, which can be therapeutic targets to overcome the refractoriness posed by tumor heterogeneity.

Simultaneous deep transcriptome and proteome profiling in a single mouse oocyte

Although single-cell multi-omics technologies are undergoing rapid development, simultaneous transcriptome and proteome analysis of a single-cell individual still faces great challenges. Here, we developed a single-cell simultaneous transcriptome and proteome (scSTAP) analysis platform based on microfluidics, high-throughput sequencing, and mass spectrometry technology to achieve deep and joint quantitative analysis of transcriptome and proteome at the single-cell level, providing an important resource for understanding the relationship between transcription and translation in cells. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19,948 genes and 2,663 protein groups in single mouse oocytes. In particular, we analyzed the correlation of individual RNA and protein pairs, as well as the meiosis regulatory network with unprecedented depth, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, which could be productive for exploring transcriptional and translational regulatory features during oocyte meiosis.

Bioactive and chemically defined hydrogels with tunable stiffness guide cerebral organoid formation and modulate multi-omics plasticity in cerebral organoids.

Organoids are an emerging technology with great potential in human disease modelling, drug development, diagnosis, tissue engineering, and regenerative medicine. Organoids as 3D-tissue culture systems have gained special attention in the past decades due to their ability to faithfully recapitulate the complexity of organ-specific tissues. Despite considerable successes in culturing physiologically relevant organoids, their real-life applications are currently limited by challenges such as scarcity of an appropriate biomimetic matrix. Peptide amphiphiles (PAs) due to their well-defined chemistry, tunable bioactivity, and extracellular matrix (ECM)-like nanofibrous architecture represent an attractive material scaffold for organoids development. Using cerebral organoids (COs) as exemplar, we demonstrate the possibility to create bio-instructive hydrogels with tunable stiffness ranging from 0.69kPa to 2.24kPa to culture and induce COs growth. We used orthogonal chemistry involving oxidative coupling and supramolecular interactions to create two-component hydrogels integrating the bio-instructive activity and ECM-like nanofibrous architecture of a laminin-mimetic PAs (IKVAV-PA) and tunable crosslinking density of hyaluronic acid functionalized with tyramine (HA-Try). Multi-omics technology including transcriptomics, proteomics, and metabolomics reveals the induction and growth of COs in soft HA-Tyr hydrogels containing PA-IKVAV such that the COs display morphology and biomolecular signatures similar to those grown in Matrigel scaffolds. Our materials hold great promise as a safe synthetic ECM for COs induction and growth. Our approach represents a well-defined alternative to animal-derived matrices for the culture of COs and might expand the applicability of organoids in basic and clinical research. STATEMENT OF SIGNIFICANCE: Synthetic bio-instructive materials which display tissue-specific functionality and nanoscale architecture of the native extracellular matrix are attractive matrices for organoids development. These synthetic matrices are chemically defined and animal-free compared to current gold standard matrices such as Matrigel. Here, we developed hydrogel matrices with tunable stiffness, which incorporate laminin-mimetic peptide amphiphiles to grow and expand cerebral organoids. Using multi-omics tools, the present study provides exciting data on the effects of neuro-inductive cues on the biomolecular profiles of brain organoids.

Predictive Biomarkers for Immunotherapy in Gastric Cancer: Current Status and Emerging Prospects.

Gastric cancer presents substantial management challenges, and the advent of immunotherapy has ignited renewed hope among patients. Nevertheless, a significant proportion of patients do not respond to immunotherapy, and adverse events associated with immunotherapy also occur on occasion, underscoring the imperative to identify suitable candidates for treatment. Several biomarkers, including programmed death ligand-1 expression, tumor mutation burden, mismatch repair status, Epstein-Barr Virus infection, circulating tumor DNA, and tumor-infiltrating lymphocytes, have demonstrated potential in predicting the effectiveness of immunotherapy in gastric cancer. However, the quest for the optimal predictive biomarker for gastric cancer immunotherapy remains challenging, as each biomarker carries its own limitations. Recently, multi-omics technologies have emerged as promising platforms for discovering novel biomarkers that may help in selecting gastric cancer patients likely to respond to immunotherapy. The identification of reliable predictive biomarkers for immunotherapy in gastric cancer holds the promise of enhancing patient selection and improving treatment outcomes. In this review, we aim to provide an overview of clinically established biomarkers of immunotherapy in gastric cancer. Additionally, we introduce newly reported biomarkers based on multi-omics studies in the context of gastric cancer immunotherapy, thereby contributing to the ongoing efforts to refine patient stratification and treatment strategies.

Hematologic Neoplasms Associated with Down Syndrome: Cellular and Molecular Heterogeneity of the Diseases.

The molecular basis of Down syndrome (DS) predisposition to leukemia is not fully understood but involves various factors such as chromosomal abnormalities, oncogenic mutations, epigenetic alterations, and changes in selection dynamics. Myeloid leukemia associated with DS (ML-DS) is preceded by a preleukemic phase called transient abnormal myelopoiesis driven by GATA1 gene mutations and progresses to ML-DS via additional mutations in cohesin genes, CTCF, RAS, or JAK/STAT pathway genes. DS-related ALL (ALL-DS) differs from non-DS ALL in terms of cytogenetic subgroups and genetic driver events, and the aberrant expression of CRLF2, JAK2 mutations, and RAS pathway-activating mutations are frequent in ALL-DS. Recent advancements in single-cell multi-omics technologies have provided unprecedented insights into the cellular and molecular heterogeneity of DS-associated hematologic neoplasms. Single-cell RNA sequencing and digital spatial profiling enable the identification of rare cell subpopulations, characterization of clonal evolution dynamics, and exploration of the tumor microenvironment's role. These approaches may help identify new druggable targets and tailor therapeutic interventions based on distinct molecular profiles, ultimately improving patient outcomes with the potential to guide personalized medicine approaches and the development of targeted therapies.

Deciphering the toxicity-effect relationship and action patterns of traditional Chinese medicines from a smart data perspective: a comprehensive review.

In Chinese medicine, the primary considerations revolve around toxicity and effect. The clinical goal is to achieve maximize effect while minimizing toxicity. Nevertheless, both clinical and experimental research has revealed a distinct relationship between these two patterns of action in toxic Traditional Chinese Medicines (TCM). These TCM often exhibit characteristic "double-sided" or "multi-faceted" features under varying pathological conditions, transitioning between effective and toxic roles. This complexity adds a layer of challenge to unraveling the ultimate objectives of Traditional Chinese medicine. To address this complexity, various hypotheses have been proposed to explain the toxicity and effect of Traditional Chinese Medicines. These hypotheses encompass the magic shrapnel theory for effect, the adverse outcome pathway framework, and the indirect toxic theory for toxicity. This review primarily focuses on high-, medium-, and low-toxicity Traditional Chinese Medicines as listed in Chinese Pharmacopoeia. It aims to elucidate the essential intrinsic mechanisms and elements contributing to their toxicity and effectiveness. The critical factors influencing the mechanisms of toxicity and effect are the optimal dosage and duration of TCM administration. However, unraveling the toxic-effect relationships in TCM presents a formidable challenge due to its multi-target and multi-pathway mechanisms of action. We propose the integration of multi-omics technology to comprehensively analyze the fundamental metabolites, mechanisms of action, and toxic effects of TCM. This comprehensive approach can provide valuable insights into the intricate relationship between the effect and toxicity of these TCM.

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer.

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures. In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs). We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level. Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibilityand chromosomal interaction, as well as genetic variation data from 24501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods.

While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In thisarticle, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, andby eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).

Single-cell multi-omics sequencing reveals chromosome copy number inconsistency between trophectoderm and inner cell mass in human reconstituted embryos after spindle transfer.

Is the chromosome copy number of the trophectoderm (TE) of a human reconstituted embryos after spindle transfer (ST) representative of the inner cell mass (ICM)? Single-cell multi-omics sequencing revealed that ST blastocysts have a higher proportion of cell lineages exhibiting intermediate mosaicism than conventional ICSI blastocysts, and that the TE of ST blastocysts does not represent the chromosome copy number of ICM. Preimplantation genetic testing for aneuploidy (PGT-A) assumes that TE biopsies are representative of the ICM, but the TE and ICM originate from different cell lineages, and concordance between TE and ICM is not well-studied, especially in ST embryos. We recruited 30 infertile women who received treatment at our clinic and obtained 45 usable blastocysts (22 from conventional ICSI and 23 reconstituted embryos after ST). We performed single-cell multi-omics sequencing on all blastocysts to predict and verify copy number variations (CNVs) in each cell. We determined the chromosome copy number of each embryo by analysing the proportion of abnormal cells in each blastocyst. We used the Bland-Altman concordance and the Kappa test to evaluate the concordance between TE and ICM in the both groups. The study was conducted at a public tertiary hospital in China, where all the embryo operations, including oocytes retrieval, ST, and ICSI, were performed in the embryo laboratory. We utilized single-cell multi-omics sequencing technology at the Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, to analyse the blastocysts. Transcriptome sequencing was used to predict the CNV of each cell through bioinformatics analysis, and the results were validated using the DNA methylation library of each cell to confirm chromosomal normalcy. We conducted statistical analysis and graphical plotting using R 4.2.1, SPSS 27, and GraphPad Prism 9.3. Mean age of the volunteers, the blastocyst morphology, and the developmental ratewere similar in ST and ICSI groups. The blastocysts in the ST group had some additional chromosomal types that were prone to variations beyond those enriched in the blastocysts of the ICSI group. Finally, both Bland-Altman concordance test and kappa concordancetest showed good chromosomal concordance between TE and ICM in the ICSI blastocysts (kappa = 0.659, P < 0.05), but not in ST blastocysts (P = 1.000), suggesting that the TE in reconstituted embryos is not representative of ICM. Gene functional annotation (GO and KEGG analyses) suggests that there may be new or additional pathways for CNV generation in ST embryos compared to ICSI embryos. This study was mainly limited by the small sample size and the limitations of single-cell multi-omics sequencing technology. To select eligible single cells, some cells of the embryos were eliminated or not labelled, resulting in a loss of information about them. The findings of this study are innovative and exploratory. A larger sample size of human embryos (especially ST embryos) and more accurate molecular genetics techniques for detecting CNV in single cells are needed to validate our results. Our study justifies the routine clinical use of PGT-A in ICSI blastocysts, as we found that the TE is a good substitute for ICM in predicting chromosomal abnormalities. While PGT-A is not entirely accurate, our data demonstrate good clinical feasibility. This trial was able to provide correct genetic counselling to patients regarding the reliability of PGT-A. Regarding ST blastocysts, the increased mosaicism rate and the inability of the TE to represent the chromosomal copy number of the ICM are both biological characteristics that differentiate them from ICSI blastocysts. Currently, ST is not used clinically on a large scale to produce blastocysts. However, if ST becomes more widely used in the future, our study will be the first to demonstrate that the use of PGT-A in ST blastocysts may not be as accurate as PGT-A for ICSI blastocysts. This study was supported by grants from the National Key R&D Program of China (2018YFA0107601) and the National Key R&D Program of China (2018YFC1003003). The authors declare no conflict of interest. N/A.

Trends in the application of "omics" to Alzheimer's disease: a bibliometric and visualized study.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset. The widespread application of omics techniques in AD has attracted considerable attention. We aimed to make a comprehensive analysis of published omics articles on AD in order to determine the research profile and application trends of omics techniques in AD. This study utilizes bibliometric and visual methods including a map collaboration map, co-citations, and keywords to identify knowledge structures, hot topics, and research trends based on 6,828 publications from the Web of Science Core Collection (WoSCC) database. The results of this study showed that 5654 institutions from 91 countries published articles in this field. The USA, China, and the UK played a leading role in publishing numerous articles in relevant journals as well as prolific institutions and authors, respectively. This paper collects a large number of literatures on the application of AD omics technology from the WoSCC database and found the omics technology applied to AD is mainly based on genomics technology. The application of transcriptomics technology has shown an increasing trend in recent years, and the application of multi-omics technology will be the general trend in the future. The development status, frontier hotspots, and general trends of omics application technologies are reviewed. This article will provide intelligence support to researchers and institutions in the field of Alzheimer's omics research and applications from a practical perspective.

Advancements in computational modelling of biological systems: seventh annual SysMod meeting

Abstract Summary The Computational Modelling of Systems Biology (SysMod) Community of Special Interest (COSI) convenes annually at the Intelligent Systems for Molecular Biology (ISMB) conference to facilitate knowledge dissemination and exchange of research findings on systems modelling from interdisciplinary domains. The SysMod meeting 2022 was held in a hybrid mode in Madison, Wisconsin, spanning a 1-day duration centred on modelling techniques, applications, and single-cell technology implementations. The meeting showcased innovative approaches to modelling biological systems using cell-specific and multiscale modelling, multiomics data integration, and novel tools to develop systems models using single-cell and multiomics technology. The meeting also recognized outstanding research by awarding the three best posters. This report summarizes the key highlights and outcomes of the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

Perspective: Challenges and Future Directions in Clinical Research with Nuts and Berries.

Consumption of nuts and berries are considered part of a healthy eating pattern. Nuts and berries contain a complex nutrient profile, consisting of essential vitamins and minerals, fiber, polyunsaturated fatty acids, and phenolics in quantities that improve physiological outcomes. The spectrum of health outcomes that may be impacted by the consumptions of nuts and berries include cardiovascular, gut microbiome, and cognitive, among others. Recently, new insights regarding the bioactive compounds found in both nuts and berries have reinforced their role for use in precision nutrition efforts. However, challenges exist that can affect the generalizability of outcomes from clinical studies, including inconsistency in study designs, homogeneity of test populations, variability in test products and control foods, and assessing realistic portion sizes. Future research centered on precision nutrition and multi-omics technologies will yield new insights. These and other topics such as funding streams and perceived risk-of-bias were explored at an international nutrition conference focused on the role of nuts and berries in clinical nutrition. Successes, challenges and future directions with these foods are presented here.

Multi-omics analysis of fecal samples in colorectal cancer Egyptians patients: a pilot study

BackgroundColorectal cancer (CRC) is a public health concern and the second most common disease worldwide. This is due to genetic coding and is influenced by environmental aspects, in which the gut microbiota plays a significant role. The purpose of this study was to compare the microbiota makeup of CRC patients with that of healthy control and to identify upregulated and downregulated proteins and metabolites in CRC patients. Using a next-generation sequencing approach, fecal samples of five females (4 CRC patients and one healthy control) were analyzed by BGI DNBSEQ-T7, Hong Kong, China. Furthermore, proteomics and metabolomics analysis were performed using LC-MS/MS technique.ResultsDysbiosis of gut microbiota has been observed in patients with CRC, with an increase in microbiota diversity at all taxonomic levels relative to healthy control. Where, at the functional level the bacterial species participate in many different pathways among them de novo nucleotide synthesis and amino acids pathways were aberrantly upregulated in CRC patients. Proteomics and metabolomics profiles of CRC patients showed different proteins and metabolites, a total of 360 and 158 proteins and metabolites, respectively were highly expressed compared to healthy control with fold change ≥ 1.2. Among the highly expressed proteins were transketolase, sushi domain-containing protein, sulfide quinone oxidoreductase protein, AAA family ATPase protein, carbonic anhydrase, IgG Fc-binding protein, nucleoside diphosphate kinase protein, arylsulfatase, alkaline phosphatase protein, phosphoglycerate kinase, protein kinase domain-containing protein, non-specific serine/threonine protein kinase, Acyl-CoA synthetase and EF-hand domain-containing protein. Some of the differential metabolites, Taurine, Taurocholic acid, 7-ketodeoxycholic acid, Glycochenodeoxycholic acid, Glycocholic acid, and Taurochenodeoxycholic acid that belong to bile acids metabolites.ConclusionsSome bacterial species, proteins, and metabolites could be used as diagnostic biomarkers for CRC. Our study paves an insight into using multi-omics technology to address the relationship between gut microbiota and CRC.

Challenges and Future Trends in the Treatment of Psoriasis.

Psoriasis is a chronic inflammatory skin disorder, and current treatments include topical therapies, phototherapy, systemic immune modulators, and biologics, aiming to alleviate symptoms and improve quality of life. However, challenges persist, such as adverse effects, treatment resistance, high costs, and variability in response among individuals. The future of psoriasis treatment shows promising emerging trends. New biologic agents targeting novel pathways, such as interleukin 23 inhibitors like mirikizumab, offer enhanced efficacy. Small molecule inhibitors like RORγt inhibitors and ROCK2 inhibitors provide additional treatment options. Combination therapies, including biologics with methotrexate, may improve treatment response. Advancements in topical treatments utilizing microneedles and nanoparticle-based carriers can enhance drug delivery and improve therapeutic outcomes. Biomarkers and multi-omics technologies hold potential for personalized treatment approaches, thus aiding in diagnosis, predicting treatment response, and guiding therapeutic decisions. Collaboration among researchers, clinicians, and industry stakeholders is crucial to translating these scientific breakthroughs into clinical practice. By addressing current challenges and exploring these promising trends, we can optimize psoriasis management and improve the lives of those affected by this chronic condition.

Interaction analysis of tobacco leaf microbial community structure and volatiles flavor compounds during cigar stacking fermentation.

Cigar stacking fermentation is a key step in tobacco aroma enhancement and miscellaneous gas reduction, which both have a great influence on increasing cigar flavor and improving industrial availability. To analyze the effect of cigar tobacco leaf (CTLs) microbial community on volatiles flavor compounds (VFCs), this study used multi-omics technology to reveal the changes in microbial community structure and VFCs of different cigar varieties during stacking fermentation, in addition to exploring the interaction mechanism of microbiome and VFCs. The results showed that the dominant microbial compositions of different CTL varieties during stacking fermentation were similar, which included Staphylococcus, Corynebacterium 1, Aerococcus, and Aspergillus. These dominant microbes mainly affected the microbial community structure and characteristic microorganisms of CTLs through microbial interactions, thereby influencing the transformation of VFCs. Characteristic microorganisms of different CTLs varieties such as Trichothecium, Trichosporon, Thioalkalicoccus and Jeotgalicoccus, were found to positively correlate with characteristic VFCs like megastigmatrienone 4, pyrazine, tetramethyl-, geranyl acetone, and 2-undecanone, 6,10-dimethyl-, respectively. This in turn affected the aroma and sensory quality of the CTLs. This study provides theoretical support for the analysis of the mechanism of microorganisms on VFCs and aroma, and development of microbial agents during cigar stacking fermentation.

A fast chemical reprogramming system promotes cell identity transition through a diapause-like state.

Cellular reprogramming by only small molecules holds enormous potentials for regenerative medicine. However, chemical reprogramming remains a slow process and labour intensive, hindering its broad applications and the investigation of underlying molecular mechanisms. Here, through screening of over 21,000 conditions, we develop a fast chemical reprogramming (FCR) system, which significantly improves the kinetics of cell identity rewiring. We find that FCR rapidly goes through an interesting route for pluripotent reprogramming, uniquely transitioning through a developmentally diapause-like state. Furthermore, FCR critically enables comprehensive characterizations using multi-omics technologies, and has revealed unexpected important features including key regulatory factors and epigenetic dynamics. Particularly, activation of pluripotency-related endogenous retroviruses via inhibition of heterochromatin significantly enhances reprogramming. Our studies provide critical insights into how only environmental cues are sufficient to rapidly reinstate pluripotency in somatic cells, and make notable technical and conceptual advances for solving the puzzle of regeneration.

Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA+ scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Applications of multi-omics analysis in human diseases.

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Contrastively generative self-expression model for single-cell and spatial multimodal data.

Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-expression relationship to consider the characteristics of different omics data by deep self-expressive generative model. Then, scMSI combines these omics-specific self-expression relations through contrastive learning. In such a way, scMSI provides a paradigm to integrate multiple omics data even with weak relation, which effectively achieves the representation learning and data integration into a unified framework. We demonstrate that scMSI provides a cohesive solution for a variety of analysis tasks, such as integration analysis, data denoising, batch correction and spatial domain detection. We have applied scMSI on various single-cell and spatial multimodal datasets to validate its high effectiveness and robustness in diverse data types and application scenarios.