Structural elements promote architectural stripe formation and facilitate ultra-long-range gene regulation at a human disease locus.

Enhancer clusters overlapping disease-associated mutations in Pierre Robin sequence (PRS) patients regulate SOX9 expression at genomic distances over 1.25 Mb. We applied optical reconstruction of chromatin architecture (ORCA) imaging to trace 3D locus topology during PRS-enhancer activation. We observed pronounced changes in locus topology between cell types. Subsequent analysis of single-chromatin fiber traces revealed that these ensemble-average differences arise through changes in the frequency of commonly sampled topologies. We further identified two CTCF-bound elements, internal to the SOX9 topologically associating domain, which promote stripe formation, are positioned near the domain's 3D geometric center, and bridge enhancer-promoter contacts in a series of chromatin loops. Ablation of these elements results in diminished SOX9 expression and altered domain-wide contacts. Polymer models with uniform loading across the domain and frequent cohesin collisions recapitulate this multi-loop, centrally clustered geometry. Together, we provide mechanistic insights into architectural stripe formation and gene regulation over ultra-long genomic ranges.

Co-transcriptional genome surveillance by HUSH is coupled to termination machinery.

The HUSH complex recognizes and silences foreign DNA such as viruses, transposons, and transgenes without prior exposure to its targets. Here, we show that endogenous targets of the HUSH complex fall into two distinct classes based on the presence or absence of H3K9me3. These classes are further distinguished by their transposon content and differential response to the loss of HUSH. A de novo genomic rearrangement at the Sox2 locus induces a switch from H3K9me3-independent to H3K9me3-associated HUSH targeting, resulting in silencing. We further demonstrate that HUSH interacts with the termination factor WDR82 and-via its component MPP8-with nascent RNA. HUSH accumulates at sites of high RNAPII occupancy including long exons and transcription termination sites in a manner dependent on WDR82 and CPSF. Together, our results uncover the functional diversity of HUSH targets and show that this vertebrate-specific complex exploits evolutionarily ancient transcription termination machinery for co-transcriptional chromatin targeting and genome surveillance.

ZDHHC5-mediated NLRP3 palmitoylation promotes NLRP3-NEK7 interaction and inflammasome activation.

The nucleotide-binding domain (NBD), leucine-rich repeat (LRR), and pyrin domain (PYD)-containing protein 3 (NLRP3) inflammasome is a critical mediator of the innate immune response. How NLRP3 responds to stimuli and initiates the assembly of the NLRP3 inflammasome is not fully understood. Here, we found that a cellular metabolite, palmitate, facilitates NLRP3 activation by enhancing its S-palmitoylation, in synergy with lipopolysaccharide stimulation. NLRP3 is post-translationally palmitoylated by zinc-finger and aspartate-histidine-histidine-cysteine 5 (ZDHHC5) at the LRR domain, which promotes NLRP3 inflammasome assembly and activation. Silencing ZDHHC5 blocks NLRP3 oligomerization, NLRP3-NEK7 interaction, and formation of large intracellular ASC aggregates, leading to abrogation of caspase-1 activation, IL-1ß/18 release, and GSDMD cleavage, both in human cells and in mice. ABHD17A depalmitoylates NLRP3, and one human-heritable disease-associated mutation in NLRP3 was found to be associated with defective ABHD17A binding and hyper-palmitoylation. Furthermore, Zdhhc5-/- mice showed defective NLRP3 inflammasome activation in vivo. Taken together, our data reveal an endogenous mechanism of inflammasome assembly and activation and suggest NLRP3 palmitoylation as a potential target for the treatment of NLRP3 inflammasome-driven diseases.

Energy sensor AMPK gamma regulates translation via phosphatase PPP6C independent of AMPK alpha.

Maintenance of energy level to drive movements and material exchange with the environment is a basic principle of life. AMP-activated protein kinase (AMPK) senses energy level and is a major regulator of cellular energy responses. The gamma subunit of AMPK senses elevated ratio of AMP to ATP and allosterically activates the alpha catalytic subunit to phosphorylate downstream effectors. Here, we report that knockout of AMPKγ, but not AMPKα, suppressed phosphorylation of eukaryotic translation elongation factor 2 (eEF2) induced by energy starvation. We identified PPP6C as an AMPKγ-regulated phosphatase of eEF2. AMP-bound AMPKγ sequesters PPP6C, thereby blocking dephosphorylation of eEF2 and thus inhibiting translation elongation to preserve energy and to promote cell survival. Further phosphoproteomic analysis identified additional targets of PPP6C regulated by energy stress in an AMPKγ-dependent manner. Thus, AMPKγ senses cellular energy availability to regulate not only AMPKα kinase, but also PPP6C phosphatase and possibly other effectors.

NLRP12 attenuates colon inflammation by maintaining colonic microbial diversity and promoting protective commensal bacterial growth.

Inflammatory bowel diseases involve the dynamic interaction of host genetics, the microbiome and inflammatory responses. Here we found lower expression of NLRP12 (which encodes a negative regulator of innate immunity) in human ulcerative colitis, by comparing monozygotic twins and other patient cohorts. In parallel, Nlrp12 deficiency in mice caused increased basal colonic inflammation, which led to a less-diverse microbiome and loss of protective gut commensal strains (of the family Lachnospiraceae) and a greater abundance of colitogenic strains (of the family Erysipelotrichaceae). Dysbiosis and susceptibility to colitis associated with Nlrp12 deficency were reversed equally by treatment with antibodies targeting inflammatory cytokines and by the administration of beneficial commensal Lachnospiraceae isolates. Fecal transplants from mice reared in specific-pathogen-free conditions into germ-free Nlrp12-deficient mice showed that NLRP12 and the microbiome each contributed to immunological signaling that culminated in colon inflammation. These findings reveal a feed-forward loop in which NLRP12 promotes specific commensals that can reverse gut inflammation, while cytokine blockade during NLRP12 deficiency can reverse dysbiosis.

Corrigendum: NLRP12 attenuates colon inflammation by maintaining colonic microbial diversity and promoting protective commensal bacterial growth.

This corrects the article DOI: 10.1038/ni.3690.

METTL3 and N6-Methyladenosine Promote Homologous Recombination-Mediated Repair of DSBs by Modulating DNA-RNA Hybrid Accumulation.

Double-strand breaks (DSBs) are the most deleterious DNA lesions, which, if left unrepaired, may lead to genome instability or cell death. Here, we report that, in response to DSBs, the RNA methyltransferase METTL3 is activated by ATM-mediated phosphorylation at S43. Phosphorylated METTL3 is then localized to DNA damage sites, where it methylates the N6 position of adenosine (m6A) in DNA damage-associated RNAs, which recruits the m6A reader protein YTHDC1 for protection. In this way, the METTL3-m6A-YTHDC1 axis modulates accumulation of DNA-RNA hybrids at DSBs sites, which then recruit RAD51 and BRCA1 for homologous recombination (HR)-mediated repair. METTL3-deficient cells display defective HR, accumulation of unrepaired DSBs, and genome instability. Accordingly, depletion of METTL3 significantly enhances the sensitivity of cancer cells and murine xenografts to DNA damage-based therapy. These findings uncover the function of METTL3 and YTHDC1 in HR-mediated DSB repair, which may have implications for cancer therapy.

Systematic identification and characterization of exon-intron circRNAs.

Exon-intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5'-end, whereas LIRs are closer to the 3'-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis of EIciLIMK1, which enhances the expression of LIMK1 in cis to boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

A discrete chromatin loop in the mouse Tcra-Tcrd locus shapes the TCRδ and TCRα repertoires.

The locus encoding the T cell antigen receptor (TCR) α-chain and δ-chain (Tcra-Tcrd) undergoes recombination of its variable-diversity-joining (V(D)J) segments in CD4(-)CD8(-) double-negative thymocytes and CD4(+)CD8(+) double-positive thymocytes to generate diverse TCRδ repertoires and TCRα repertoires, respectively. Here we identified a chromatin-interaction network in the Tcra-Tcrd locus in double-negative thymocytes that was formed by interactions between binding elements for the transcription factor CTCF. Disruption of a discrete chromatin loop encompassing the D, J and constant (C) segments of Tcrd allowed a single V segment to frequently contact and rearrange to D and J segments and dominate the adult TCRδ repertoire. Disruption of this loop also narrowed the TCRα repertoire, which, we believe, followed as a consequence of the restricted TCRδ repertoire. Hence, a single CTCF-mediated chromatin loop directly regulated TCRδ diversity and indirectly regulated TCRα diversity.

The Innate Immune Sensor NLRC3 Acts as a Rheostat that Fine-Tunes T Cell Responses in Infection and Autoimmunity.

Appropriate immune responses require a fine balance between immune activation and attenuation. NLRC3, a non-inflammasome-forming member of the NLR innate immune receptor family, attenuates inflammation in myeloid cells and proliferation in epithelial cells. T lymphocytes express the highest amounts of Nlrc3 transcript where its physiologic relevance is unknown. We show that NLRC3 attenuated interferon-γ and TNF expression by CD4+ T cells and reduced T helper 1 (Th1) and Th17 cell proliferation. Nlrc3-/- mice exhibited increased and prolonged CD4+ T cell responses to lymphocytic choriomeningitis virus infection and worsened experimental autoimmune encephalomyelitis (EAE). These functions of NLRC3 were executed in a T-cell-intrinsic fashion: NLRC3 reduced K63-linked ubiquitination of TNF-receptor-associated factor 6 (TRAF6) to limit NF-κB activation, lowered phosphorylation of eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), and diminished glycolysis and oxidative phosphorylation. This study reveals an unappreciated role for NLRC3 in attenuating CD4+ T cell signaling and metabolism.

AIM2 in regulatory T cells restrains autoimmune diseases.

The inflammasome initiates innate defence and inflammatory responses by activating caspase-1 and pyroptotic cell death in myeloid cells1,2. It consists of an innate immune receptor/sensor, pro-caspase-1, and a common adaptor molecule, ASC. Consistent with their pro-inflammatory function, caspase-1, ASC and the inflammasome component NLRP3 exacerbate autoimmunity during experimental autoimmune encephalomyelitis by enhancing the secretion of IL-1ß and IL-18 in myeloid cells3-6. Here we show that the DNA-binding inflammasome receptor AIM27-10 has a T cell-intrinsic and inflammasome-independent role in the function of T regulatory (Treg) cells. AIM2 is highly expressed by both human and mouse Treg cells, is induced by TGFß, and its promoter is occupied by transcription factors that are associated with Treg cells such as RUNX1, ETS1, BCL11B and CREB. RNA sequencing, biochemical and metabolic analyses demonstrated that AIM2 attenuates AKT phosphorylation, mTOR and MYC signalling, and glycolysis, but promotes oxidative phosphorylation of lipids in Treg cells. Mechanistically, AIM2 interacts with the RACK1-PP2A phosphatase complex to restrain AKT phosphorylation. Lineage-tracing analysis demonstrates that AIM2 promotes the stability of Treg cells during inflammation. Although AIM2 is generally accepted as an inflammasome effector in myeloid cells, our results demonstrate a T cell-intrinsic role of AIM2 in restraining autoimmunity by reducing AKT-mTOR signalling and altering immune metabolism to enhance the stability of Treg cells.

Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 γ-ray Galactic sources.

The extension of the cosmic-ray spectrum beyond 1 petaelectronvolt (PeV; 1015 electronvolts) indicates the existence of the so-called PeVatrons-cosmic-ray factories that accelerate particles to PeV energies. We need to locate and identify such objects to find the origin of Galactic cosmic rays1. The principal signature of both electron and proton PeVatrons is ultrahigh-energy (exceeding 100 TeV) γ radiation. Evidence of the presence of a proton PeVatron has been found in the Galactic Centre, according to the detection of a hard-spectrum radiation extending to 0.04 PeV (ref. 2). Although γ-rays with energies slightly higher than 0.1 PeV have been reported from a few objects in the Galactic plane3-6, unbiased identification and in-depth exploration of PeVatrons requires detection of γ-rays with energies well above 0.1 PeV. Here we report the detection of more than 530 photons at energies above 100 teraelectronvolts and up to 1.4 PeV from 12 ultrahigh-energy γ-ray sources with a statistical significance greater than seven standard deviations. Despite having several potential counterparts in their proximity, including pulsar wind nebulae, supernova remnants and star-forming regions, the PeVatrons responsible for the ultrahigh-energy γ-rays have not yet been firmly localized and identified (except for the Crab Nebula), leaving open the origin of these extreme accelerators.

Genomic insights into the formation of human populations in East Asia.

The deep population history of East Asia remains poorly understood owing to a lack of ancient DNA data and sparse sampling of present-day people1,2. Here we report genome-wide data from 166 East Asian individuals dating to between 6000 BC and AD 1000 and 46 present-day groups. Hunter-gatherers from Japan, the Amur River Basin, and people of Neolithic and Iron Age Taiwan and the Tibetan Plateau are linked by a deeply splitting lineage that probably reflects a coastal migration during the Late Pleistocene epoch. We also follow expansions during the subsequent Holocene epoch from four regions. First, hunter-gatherers from Mongolia and the Amur River Basin have ancestry shared by individuals who speak Mongolic and Tungusic languages, but do not carry ancestry characteristic of farmers from the West Liao River region (around 3000 BC), which contradicts theories that the expansion of these farmers spread the Mongolic and Tungusic proto-languages. Second, farmers from the Yellow River Basin (around 3000 BC) probably spread Sino-Tibetan languages, as their ancestry dispersed both to Tibet-where it forms approximately 84% of the gene pool in some groups-and to the Central Plain, where it has contributed around 59-84% to modern Han Chinese groups. Third, people from Taiwan from around 1300 BC to AD 800 derived approximately 75% of their ancestry from a lineage that is widespread in modern individuals who speak Austronesian, Tai-Kadai and Austroasiatic languages, and that we hypothesize derives from farmers of the Yangtze River Valley. Ancient people from Taiwan also derived about 25% of their ancestry from a northern lineage that is related to, but different from, farmers of the Yellow River Basin, which suggests an additional north-to-south expansion. Fourth, ancestry from Yamnaya Steppe pastoralists arrived in western Mongolia after around 3000 BC but was displaced by previously established lineages even while it persisted in western China, as would be expected if this ancestry was associated with the spread of proto-Tocharian Indo-European languages. Two later gene flows affected western Mongolia: migrants after around 2000 BC with Yamnaya and European farmer ancestry, and episodic influences of later groups with ancestry from Turan.

Structure of mavacamten-free human cardiac thick filaments within the sarcomere by cryoelectron tomography.

Heart muscle has the unique property that it can never rest; all cardiomyocytes contract with each heartbeat which requires a complex control mechanism to regulate cardiac output to physiological requirements. Changes in calcium concentration regulate the thin filament activation. A separate but linked mechanism regulates the thick filament activation, which frees sufficient myosin heads to bind the thin filament, thereby producing the required force. Thick filaments contain additional nonmyosin proteins, myosin-binding protein C and titin, the latter being the protein that transmits applied tension to the thick filament. How these three proteins interact to control thick filament activation is poorly understood. Here, we show using 3-D image reconstruction of frozen-hydrated human cardiac muscle myofibrils lacking exogenous drugs that the thick filament is structured to provide three levels of myosin activation corresponding to the three crowns of myosin heads in each 429Å repeat. In one crown, the myosin heads are almost completely activated and disordered. In another crown, many myosin heads are inactive, ordered into a structure called the interacting heads motif. At the third crown, the myosin heads are ordered into the interacting heads motif, but the stability of that motif is affected by myosin-binding protein C. We think that this hierarchy of control explains many of the effects of length-dependent activation as well as stretch activation in cardiac muscle control.

Circular RNAs in physiology and non-immunological diseases.

Circular RNAs (circRNAs) are covalently closed single-stranded RNAs. Four subclasses of circRNAs have been identified in animal cells, and they have unique features in their biogenesis, degradation, and transport. CircRNAs have diverse molecular functions in sponging miRNAs, regulating transcription, modulating RNA-binding proteins, and even encoding proteins. Some circRNAs are important regulators of various physiological processes to maintain homeostasis. Dysregulation of circRNAs is associated with human disorders, and individual circRNAs are crucial factors that contribute to major diseases including non-immunological diseases such as cancers, neurological disorders, cardiovascular disease, and metabolic disease. Debates on circRNAs have also been raised in recent years, and further studies would help to resolve these disputes and potentially lead to biomedical applications of circRNAs.

Eukaryotic translation initiation factor eIF4E-5 is required for spermiogenesis in Drosophila melanogaster.

Drosophila sperm development is characterized by extensive post-transcriptional regulation whereby thousands of transcripts are preserved for translation during later stages. A key step in translation initiation is the binding of eukaryotic initiation factor 4E (eIF4E) to the 5' mRNA cap. In addition to canonical eIF4E-1, Drosophila has multiple eIF4E paralogs, including four (eIF4E-3, -4, -5, and -7) that are highly expressed in the testis. Among these, only eIF4E-3 has been characterized genetically. Here, using CRISPR/Cas9 mutagenesis, we determined that eIF4E-5 is essential for male fertility. eIF4E-5 protein localizes to the distal ends of elongated spermatid cysts, and eIF4E-5 mutants exhibit defects during post-meiotic stages, including a mild defect in spermatid cyst polarization. eIF4E-5 mutants also have a fully penetrant defect in individualization, resulting in failure to produce mature sperm. Indeed, our data indicate that eIF4E-5 regulates non-apoptotic caspase activity during individualization by promoting local accumulation of the E3 ubiquitin ligase inhibitor Soti. Our results further extend the diversity of non-canonical eIF4Es that carry out distinct spatiotemporal roles during spermatogenesis.

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.

scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data.

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.

MOTIVATION: Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. RESULTS: To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.