Special Mini-Issue: Quantitative methods to decipher cellular heterogeneity - from single-cell to spatial omic methods.

In this mini-issue, we have a collection of eight reviews that discuss various advanced topics on the investigation of cellular heterogeneity. These reviews highlight the latest developments in technologies that capture and assess biology at single cell resolution, as well as approaches for cellular measurements with spatial information. Challenges and opportunities to develop future innovations and approaches are also presented.

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

Parentage influence on gene expression under acidification revealed through single-embryo sequencing.

The dissolution of anthropogenic carbon dioxide (CO2 ) in seawater has altered its carbonate chemistry in the process of ocean acidification (OA). OA affects the viability of marine species. In particular, calcifying organisms and their early planktonic larval stages are considered vulnerable. These organisms often utilize energy reserves for metabolism rather than growth and calcification as supported by bulk RNA-sequencing (RNA-seq) experiments. Yet, transcriptomic profiling of a bulk sample reflects the average gene expression of the population, neglecting the variations between individuals, which forms the basis for natural selection. Here, we used single-embryo RNA-seq on larval sea urchin Heliocidaris crassispina, which is a commercially and ecologically valuable species in East Asia, to document gene expression changes to OA at an individual and family level. Three paternal half-sibs groups were fertilized and exposed to 3 pH conditions (ambient pH 8.0, 7.7 and 7.4) for 12 h prior to sequencing and oxygen consumption assay. The resulting transcriptomic profile of all embryos can be distinguished into four clusters, with differences in gene expressions that govern biomineralization, cell differentiation and patterning, as well as metabolism. While these responses were influenced by pH conditions, the male identities also had an effect. Specifically, a regression model and goodness of fit tests indicated a significant interaction between sire and pH on the probability of embryo membership in different clusters of gene expression. The single-embryo RNA-seq approach is promising in climate stressor research because not only does it highlight potential impacts before phenotypic changes were observed, but it also highlights variations between individuals and lineages, thus enabling a better determination of evolutionary potential.

Identifying predictors of glioma evolution from longitudinal sequencing.

Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.

Profiling Single-Cell Genome and Transcriptome by scONE-Seq.

Single-cell multi-omics technologies can provide a unique perspective on tumor cellular heterogeneity. We have developed a versatile method for simultaneous transcriptome and genome profiling of single cells or single nuclei in one tube reaction, named scONE-seq. It is conveniently compatible with frozen tissue from biobanks, which are a major source of patient samples for research. Here, we describe the detailed procedures to profile single-cell/nucleus transcriptome and genome. The sequencing library is compatible with both Illumina and MGI sequencers; it is also compatible with frozen tissue from biobanks, which are a major source of patient samples for research and drug discovery.

Vascularized human brain organoid on-chip.

Modelling the human brain in vitro has been extremely challenging due to the brain's intricate cellular composition and specific structural architecture. The recent emergence of brain organoids that recapitulate many key features of human brain development has thus piqued the interest of many to further develop and apply this in vitro model for various physiological and pathological investigations. Despite ongoing efforts, the existing brain organoids demonstrate several limitations, such as the lack of a functional human vasculature with perfusion capability. Microfluidics is suited to enhance such brain organoid models by enabling vascular perfusion and a curated blood-brain barrier microenvironment. In this review, we first provide an introduction to in vivo human brain development and present the state-of-the-art in vitro human brain models. We further elaborate on different strategies to improve the vascularized human brain organoid microenvironment using microfluidic devices, while discussing the current obstacles and future directions in this field.

Assessment of microbiota in the gut and upper respiratory tract associated with SARS-CoV-2 infection.

BACKGROUND: The human microbiome plays an important role in modulating the host metabolism and immune system. Connections and interactions have been found between the microbiome of the gut and oral pharynx in the context of SARS-CoV-2 and other viral infections; hence, to broaden our understanding of host-viral responses in general and to deepen our knowledge of COVID-19, we performed a large-scale, systematic evaluation of the effect of SARS-CoV-2 infection on human microbiota in patients with varying disease severity. RESULTS: We processed 521 samples from 203 COVID-19 patients with varying disease severity and 94 samples from 31 healthy donors, consisting of 213 pharyngeal swabs, 250 sputa, and 152 fecal samples, and obtained meta-transcriptomes as well as SARS-CoV-2 sequences from each sample. Detailed assessment of these samples revealed altered microbial composition and function in the upper respiratory tract (URT) and gut of COVID-19 patients, and these changes are significantly associated with disease severity. Moreover, URT and gut microbiota show different patterns of alteration, where gut microbiome seems to be more variable and in direct correlation with viral load; and microbial community in the upper respiratory tract renders a high risk of antibiotic resistance. Longitudinally, the microbial composition remains relatively stable during the study period. CONCLUSIONS: Our study has revealed different trends and the relative sensitivity of microbiome in different body sites to SARS-CoV-2 infection. Furthermore, while the use of antibiotics is often essential for the prevention and treatment of secondary infections, our results indicate a need to evaluate potential antibiotic resistance in the management of COVID-19 patients in the ongoing pandemic. Moreover, a longitudinal follow-up to monitor the restoration of the microbiome could enhance our understanding of the long-term effects of COVID-19. Video Abstract.

scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors.

Single-cell multi-omics can provide a unique perspective on tumor cellular heterogeneity. Most previous single-cell whole-genome RNA sequencing (scWGS-RNA-seq) methods demonstrate utility with intact cells from fresh samples. Among them, many are not applicable to frozen samples that cannot produce intact single-cell suspensions. We have developed scONE-seq, a versatile scWGS-RNA-seq method that amplifies single-cell DNA and RNA without separating them from each other and hence is compatible with frozen biobanked samples. We benchmarked scONE-seq against existing methods using fresh and frozen samples to demonstrate its performance in various aspects. We identified a unique transcriptionally normal-like tumor clone by analyzing a 2-year frozen astrocytoma sample, demonstrating that performing single-cell multi-omics interrogation on biobanked tissue by scONE-seq could enable previously unidentified discoveries in tumor biology.

Driving axon regeneration by orchestrating neuronal and non-neuronal innate immune responses via the IFNγ-cGAS-STING axis.

The coordination mechanism of neural innate immune responses for axon regeneration is not well understood. Here, we showed that neuronal deletion of protein tyrosine phosphatase non-receptor type 2 sustains the IFNγ-STAT1 activity in retinal ganglion cells (RGCs) to promote axon regeneration after injury, independent of mTOR or STAT3. DNA-damage-induced cGAMP synthase (cGAS)-stimulator of interferon genes (STINGs) activation is the functional downstream signaling. Directly activating neuronal STING by cGAMP promotes axon regeneration. In contrast to the central axons, IFNγ is locally translated in the injured peripheral axons and upregulates cGAS expression in Schwann cells and infiltrating blood cells to produce cGAMP, which promotes spontaneous axon regeneration as an immunotransmitter. Our study demonstrates that injured peripheral nervous system (PNS) axons can direct the environmental innate immune response for self-repair and that the neural antiviral mechanism can be harnessed to promote axon regeneration in the central nervous system (CNS).

Transcriptomic analysis of 3D vasculature-on-a-chip reveals paracrine factors affecting vasculature growth and maturation.

In vitro models of vasculature are of great importance for modelling vascular physiology and pathology. However, there is usually a lack of proper spatial patterning of interacting heterotypic cells in conventional vasculature dish models, which might confound results between contact and non-contact interactions. We use a microfluidic platform with structurally defined separation between human microvasculature and fibroblasts to probe their dynamic, paracrine interactions. We also develop a novel, versatile technique to retrieve cells embedded in extracellular matrix from the microfluidic device for downstream transcriptomic analysis, and uncover growth factor and cytokine expression profiles associated with improved vasculature growth. Paired receptor-ligand analysis further reveals paracrine signaling molecules that could be supplemented into the medium for vasculatures models where fibroblast coculture is undesirable or infeasible. These findings also provide deeper insights into the molecular cues for more physiologically relevant vascular mimicry and vascularized organoid model for clinical applications such as drug screening and disease modeling.

Cell-Free DNA as Biomarker for Sepsis by Integration of Microbial and Host Information.

BACKGROUND: Cell-free DNA (cfDNA) is emerging as a biomarker for sepsis. Previous studies have been focused mainly on identifying blood infections or simply quantifying cfDNA. We propose that by characterizing multifaceted unexplored components, cfDNA could be more informative for assessing this complex syndrome. METHODS: We explored multiple aspects of cfDNA in septic and nonseptic intensive care unit (ICU) patients by metagenomic sequencing, with longitudinal measurement and integrative assessment of plasma cfDNA quantity, human cfDNA fragmentation patterns, infecting pathogens, and overall microbial composition. RESULTS: Septic patients had significantly increased cfDNA quantity and altered human cfDNA fragmentation pattern. Moreover, human cfDNA fragments appeared to comprise information about cellular oxidative stress and could indicate disease severity. Metagenomic sequencing was more sensitive than blood culture in detecting bacterial infections and allowed for simultaneous detection of viral pathogens. We found differences in microbial composition between septic and nonseptic patients and between survivors and nonsurvivors by 28-day mortality, both on the first day of ICU admission and across the study period. By integrating all the information into a machine learning model, we achieved improved performance in identifying sepsis and prediction of clinical outcome for ICU patients with areas under the curve of 0.992 (95% CI 0.969-1.000) and 0.802 (95% CI 0.605-0.999), respectively. CONCLUSIONS: We were able to diagnose sepsis and predict mortality as soon as the first day of ICU admission by integrating multifaceted cfDNA information obtained in a single metagenomic assay; this approach could provide important advantages for clinical management and for improving outcomes in ICU patients.

Cell fate determining molecular switches and signaling pathways in Pax7-expressing somitic mesoderm.

During development, different cell types originate from a common progenitor at well-defined time points. Previous lineage-tracing of Pax7+ progenitors from the somitic mesoderm has established its developmental trajectory towards the dermis, brown adipocytes, and skeletal muscle in the dorsal trunk; yet the molecular switches and mechanisms guiding the differentiation into different lineages remain unknown. We performed lineage-tracing of Pax7-expressing cells in mouse embryos at E9.5 and profiled the transcriptomes of Pax7-progenies on E12.5, E14.5, and E16.5 at single-cell level. Analysis of single-cell transcriptomic data at multiple time points showed temporal-specific differentiation events toward muscle, dermis, and brown adipocyte, identified marker genes for putative progenitors and revealed transcription factors that could drive lineage-specific differentiation. We then utilized a combination of surface markers identified in the single-cell data, Pdgfra, Thy1, and Cd36, to enrich brown adipocytes, dermal fibroblasts, and progenitors specific for these two cell types at E14.5 and E16.5. These enriched cell populations were then used for further culture and functional assays in vitro, in which Wnt5a and Rgcc are shown to be important factors that could alter lineage decisions during embryogenesis. Notably, we found a bipotent progenitor population at E14.5, having lineage potentials towards both dermal fibroblasts and brown adipocytes. They were termed eFAPs (embryonic fibro/adipogenic progenitors) as they functionally resemble adult fibro/adipogenic progenitors. Overall, this study provides further understanding of the Pax7 lineage during embryonic development using a combination of lineage tracing with temporally sampled single-cell transcriptomics.

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets.

Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. We present FIRM, a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. Compared with existing state-of-the-art integration methods, FIRM provides accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. FIRM is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.

Auditing the Editor: A Review of Key Translational Issues in Epigenetic Editing.

Currently, most advances in site-specific epigenetic editing for human use are concentrated in basic research, yet, there is considerable interest to translate this technology beyond the bench. This review highlights recent developments with epigenetic editing technology in comparison with the canonical CRISPR-Cas genome editing, as well as the epistemic and ethical considerations with preemptive translation of epigenetic editing into clinical or commercial use in humans. Key considerations in safety, equity, and access to epigenetic editing are highlighted, with a spotlight on the ethical, legal, and social issues of this technology in the context of global health equity.

Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets.

The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We have created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. When compared to other state-of-the-art methods, Portal achieves better performance for preserving biological variation during integration, while achieving the integration of millions of cells, in minutes, with low memory consumption. We show that Portal is widely applicable to integrating datasets across different samples, platforms and data types. We also apply Portal to the integration of cross-species datasets with limited shared information among them, elucidating biological insights into the similarities and divergences in the spermatogenesis process among mouse, macaque and human.

The effect of methanol fixation on single-cell RNA sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. RESULTS: We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. CONCLUSIONS: Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Microfluidic single-cell analysis-Toward integration and total on-chip analysis.

Various types of single-cell analyses are now extensively used to answer many biological questions, and with this growth in popularity, potential drawbacks to these methods are also becoming apparent. Depending on the specific application, workflows can be laborious, low throughput, and run the risk of contamination. Microfluidic designs, with their advantages of being high throughput, low in reaction volume, and compatible with bio-inert materials, have been widely used to improve single-cell workflows in all major stages of single-cell applications, from cell sorting to lysis, to sample processing and readout. Yet, designing an integrated microfluidic chip that encompasses the entire single-cell workflow from start to finish remains challenging. In this article, we review the current microfluidic approaches that cover different stages of processing in single-cell analysis and discuss the prospects and challenges of achieving a full integrated workflow to achieve total single-cell analysis in one device.