Biology subjects

Fang, T.

Publications and source records attributed to Fang, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Evolving stem cell fate capacities and transcriptional priming in the developing olfactory epithelium

The olfactory epithelium of adult mammals contains two populations of stem cells that support its remarkable ability to regenerate neuronal and non-neuronal cell types throughout life. How olfactory epithelial cell types are established during development, however, is not well understood. Here, we use genetic lineage tracing and single-cell RNA sequencing of the perinatal mouse olfactory epithelium to construct a developmental trajectory consisting of multiple lineages. We identify transitional states and lineage relationships between individual cells and establish Ascl1+ cells as the primary multipotent progenitors in the perinatal olfactory epithelium. Further, Ascl1+ cells become progressively restricted in their cell fate capacity over developmental time and appear to be transcriptionally primed toward specific lineages. We also predict signaling pathways that may contribute to lineage plasticity and niche permissiveness. Together, these results contribute to our understanding of how cell-intrinsic and-extrinsic signals contribute to the establishment of a stem cell niche.

developmental biology↗

bioRxiv2026

scWeave: A deep learning model that bidirectionally translates between gene expression and chromatin structure at single cell resolution

Chromatin structure and gene expression are intimately linked, yet characterizing how the two covary has proven challenging, primarily because the two modalities are rarely measured in the same cells. Recently, single-cell co-assay protocols have enabled simultaneous profiling of both modalities within the same cells, but these experiments remain costly and technically challenging. To better characterize the relationship between 3D chromatin architecture and gene expression and to enable cross-modality inference from single-modality measurements, we developed a model called scWeave that bidirectionally translates between gene expression (scRNA-seq) and 3D chromatin architecture (scHi-C) at single-cell resolution. The scWeave model employs dual autoencoders to extract separate cell-level latent representations and learns to translate between these representations using dedicated translation modules. We evaluate scWeave on six publicly available co-assay datasets spanning mouse embryonic development, mouse cortex, mouse olfactory epithelium, and human bone marrow. On held-out mouse cells, scWeave outperforms a nearest-neighbor baseline and existing methods adapted to single-cell resolution, achieving a 57% improvement in median Spearman correlation when predicting gene expression from chromatin structure and an 18.8% improvement in median HiCRep similarity in the reverse direction relative to the next-best baseline. We further show that scWeave learns cross-modally aligned latent representations at single-cell resolution, enabling cells profiled in one modality to be matched to their counterparts in the other. Finally, scWeave generalizes to entirely held-out developmental timepoints in mouse olfactory epithelium and performs well on held-out human bone marrow cells despite limited human training data. By predicting the unmeasured chromatin architecture or transcriptional state from a single measured modality, scWeave offers a route to extend the benefits of costly co-assays to the many cell types, developmental stages, and species that are currently profiled with only one modality.

genomics↗

bioRxiv2025

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Gene expression is governed by both linear DNA sequence and three-dimensional (3D) chromatin architecture. Most gene expression prediction models rely on sequence alone, thereby failing to capture structural context and to generalize to unseen cell types. We present Puget, a deep learning model that predicts cell type-specific gene expression from sequence and Hi-C data, which captures 3D chromatin organization. Puget pairs pretrained sequence and Hi-C encoders with a lightweight transformer decoder. Using paired Hi-C/RNA-seq from 36 human and 4 mouse biosamples, we evaluate Pugets ability to generalize to held-out genes, held-out biosamples, and from human to mouse. Relative to a sequence-only baseline, Puget improves cross-biosample Pearson correlation by up to 25% on highly variable genes in training biosamples and, unlike the sequence-only model, generalizes to held-out biosamples and across species. In addition, in silico perturbation experiments show that Puget can prioritize experimentally validated enhancer-gene pairs. Together, these results highlight a generalizable approach for modeling gene expression from sequence and 3D chromatin organization.

bioinformatics↗

bioRxiv2025

Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecture

Understanding how genomic sequences shape three-dimensional (3D) genome architecture is funda-mental to interpreting diverse biological processes. Although previous studies have shown that sequence information can predict 3D genome architecture, they fall short in capturing cell type-specific structures because they are trained solely on sequence inputs. The widely available Hi-C data, which contain rich structural information across biosamples, can provide complementary features to sequence data for study-ing cell type-specific architectures. Recently, DNA foundation models have demonstrated encouraging performance in capturing long-range genomic dependencies, holding promise for modeling chromatin interactions. However, the extremely high computational cost of running these models limits their applicability to Hi-C analysis, which requires genome-wide sequence embeddings. Here, we present Evo2HiC, a multimodal foundation model that jointly models genomic sequences and structures to study cell type-specific chromatin structure. The key idea of Evo2HiC is to distill a large-scale DNA foundation model, Evo 2 (7B), into a compact encoder, while guiding the distillation with Hi-C data to preserve genomic features critical for 3D genome analysis. The model supports two types of encoders, one that operates directly on DNA sequences, and a second that additionally takes as input corresponding Hi-C data. Using the DNA-only encoder and predicting Hi-C contact matrices, Evo2HiC improved Spearman correlation by 10.9% over Orca. Moreover, by jointly embedding Hi-C and sequence information Evo2HiC achieved the best overall Pearson correlation when predicting five representative epigenomic assays. Interpretation analysis of Evo2HiC revealed its ability to identify cell type-specific sequence motifs that explain changes in epigenomic signals. Finally, we demonstrated the cross-species generalizability of Evo2HiC on 177 species from the DNA Zoo dataset for Hi-C resolution enhancement. In summary, Evo2HiC is a foundation model that integrates genome sequences and 3D chromatin structure information, substantially reduces computational cost while maintaining state-of-the-art accuracy on predicting various epigenomic signals and genome architecture, enables the identification of cell type-specific motifs, and demonstrates robust generalizability across species.

bioinformatics↗

bioRxiv2025

Screening of Oyster Peptides for Anti-Muscle Atrophy Based on Machine Learning and Computer Simulation: Guided by Antioxidant Pathways

Muscle atrophy poses a serious threat to human health, with its primary pathogenic mechanisms closely linked to oxidative stress. This study focuses on the potential of oyster peptides in alleviating dexamethasone (DEX)-induced skeletal muscle atrophy and their underlying antioxidant mechanisms. Utilizing efficient integrated machine learning and computer simulation methods, a systematic screening of active peptides and mechanistic research was conducted. The results revealed that oyster peptides at concentrations of 25-50 g/mL significantly improved the decline in cell viability and myotube atrophy induced by DEX, while downregulating the expression of muscle atrophy markers Atrogin-1 and MuRF1. Through LC-MS/MS, 220 high-activity peptide sequences were identified. Following the replication and extension of the iAnOxPep integrated learning model, 15 potential antioxidant peptides were selected. Among them, the short peptide AWPGPQ demonstrated the strongest binding affinity with Keap1 and PPAR{gamma} targets. Molecular dynamics simulations confirmed its stability, suggesting that AWPGPQ may exert its dual effects--antioxidant and anti-muscle atrophy--by modulating the Keap1-Nrf2 and PPAR{gamma} signaling pathways. This study established a systematic and efficient strategy for screening natural active peptides, providing theoretical support and technical pathways for the discovery of multifunctional short peptide candidates, with significant theoretical value and application prospects.

molecular biology↗

bioRxiv2025

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR-Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities. Here, we present CRISPR-PAMdb, a comprehensive and publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial and archaeal genomes and PAM profiles from 7.4 million phage and plasmid sequences. Through spacer-protospacer alignment, we inferred consensus PAM preferences for 8,003 unique Cas9 clusters. To extend PAM discovery beyond traditional alignment-based approaches, we developed CICERO, a machine learning model predicting PAM preferences directly from Cas9 protein sequences. Built on the ESM2 protein language model and trained on the CRISPR-PAMdb database, CICERO achieved an average accuracy of 0.68 on test data and 0.75 on experimentally validated Cas9 orthologs. For Cas9 clusters where alignment-based predictions were infeasible, CICERO generated PAM profiles for an additional 50,308 Cas9 proteins, including 17,453 high-confidence predictions with accuracies above 0.86. CRISPR-PAMdb, alongside CICERO models, enables large-scale exploration of PAM diversity across Cas9 proteins, accelerating design of next-generation CRISPR-Cas9 tools for precise genome engineering.

synthetic biology↗

bioRxiv2025

Interrupting Elmsan1 repression of nuclear Acetyl-CoA production therapeutically reprograms cancer cells

Metabolites are essential substrates for epigenetic modifications. Although nuclear acetyl-CoA constitutes a small fraction of the whole cell pool, it regulates cell fate by locally providing histone acetylation substrate. Here, we combined phenotypic chemical screen and genome-wide CRISPR screen to demonstrate a nucleus-specific acetyl-CoA regulatory mechanism that can be modulated to achieve therapeutic cancer cell reprogramming. While previously thought that nucleus-localized pyruvate dehydrogenase complex (nPDC) is constitutively active, we found that nPDC is constitutively inhibited by the nuclear protein ELMSAN1 through direct interaction. Pharmacologic inhibition of the ELMSAN1-nPDC interaction derepressed nPDC activity, enhancing nuclear acetyl-CoA generation and reprogramming cancer cells to a postmitotic state with diminished cell-of-origin signatures. Reprogramming was synergistically enhanced by histone deacetylase 1/2 inhibition, resulting in inhibited tumor growth, durably suppressed tumor-initiating ability, and improved survival in multiple cancer types in vivo, including therapy-resistant sarcoma patient-derived xenografts and carcinoma cell line xenografts. Our findings highlight the potential of targeting ELMSAN1-nPDC as epigenetic cancer therapy.

cancer biology↗

bioRxiv2025

Senescence-induced reparative fibroblasts enable scarless wound healing in aged murine skin.

It has been reported that elderly individuals exhibit reduced scarring during the wound healing process compared to younger adults. However, the underlying mechanisms responsible for this phenomenon remain poorly understood. Here, we revealed that aged mice exhibited more pronounced regenerative outcomes compared to young mice, characterized by increased hair follicle numbers and collagen fiber features closer to normal skin. Single-cell sequencing identified a reparative fibroblast subpopulation (Prss35+Fib) enriched in the aged group, which promotes regeneration through communication with epithelial cells, macrophages, and T cell subpopulations via PTN and EREG signaling. Spatial transcriptomics validated this communication pattern by elucidating cell proximity and locating the regenerative niche in the upper dermis. Finally, EREG treatment significantly enhanced regenerative outcomes in young mice, while the small wound model of aged mice, lacking the reparative fibroblast and EREG signaling, failed to achieve regeneration. Collectively, our findings advance the understanding of regenerative plasticity in aging and provide new insights for designing scarless therapeutic strategies.

cell biology↗

bioRxiv2024

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species

Nuclear DNA is organized into a compact three-dimensional (3D) structure that impacts critical cellular processes. High-throughput chromosome conformation capture (Hi-C) is the most widely used method for measuring 3D genome architecture, while linear epigenomic assays, such as ATAC-seq, DNase-seq, and ChIP-seq, are extensively employed to characterize epigenomic regulation. However, the integrative analysis of chromatin interactions and associated epigenomic regulation remains challenging due to the pairwise nature of Hi-C data, mismatched resolution between Hi-C and epigenomic assays, and inconsistencies among analysis tools. Here we propose HiCFoundation, a Hi-C-based foundation model for integrative analysis linking chromatin structure to downstream regulatory function. HiCFoundation is trained from hundreds of Hi-C assays encompassing 118 million contact matrix submatrices. The model achieves state-of-the-art performance on multiple types of 3D genome analysis, including reproducibility analysis, resolution enhancement, and loop detection. We further demonstrate the models generalizability through genome architecture analysis of 316 species. Notably, by enhancing low-coverage experimental Hi-C data, HiCFoundation reveals genome-wide loop loss during differentiation of hematopoietic stem and progenitor cells (HSPCs) to neutrophils. Additionally, HiCFoundation is able to predict multiple types of epigenomic activity from Hi-C input and further interprets the link between Hi-C input and epigenomic output to reveal the relationship between chromatin conformation and genome function. Finally, HiCFoundation can analyze single-cell Hi-C data, shedding light on genome structure at single-cell resolution. HiCFoundation thus provides a unified, e"cient, generalizable, and interpretable foundation for genome architecture, single-cell and multi-omics analysis across species, paving the path for systematically studying genome 3D architecture and its regulatory mechanisms.

bioinformatics↗

bioRxiv2024

Ca2+-independent ZmCPK2 is inhibited by Ca2+-dependent ZmCPK17 during drought response in maize

Calcium oscillations are induced by different stresses. Calcium-dependent protein kinases (CDPKs/CPKs) are one major group of the plant calcium decoders that are involved in various processes including drought response. Some CPKs are calcium-independent. Here, we identified ZmCPK2 as a negative regulator of drought resistance by screening an overexpression transgenic maize pool. We found that ZmCPK2 does not bind calcium, and its activity is modulated likely in an oscillation manner during short time ABA treatment. Interestingly, ZmCPK2 interacts with and is inhibited by calcium-dependent ZmCPK17, a positive regulator of drought resistance, which is activated by ABA. ZmCPK17 activity exhibits opposite patterns relative to ZmCPK2 under ABA treatment, and could prevent the nuclear localization of ZmCPK2 when co-expressed. ZmCPK2 interacts with and phosphorylates ZmYAB15, a negative transcriptional factor for drought resistance. Our results suggest that drought stress-induced Ca2+ can be decoded by corresponding CPK17 likely in an oscillation manner, thereby promoting plant adaptation to water deficit.

molecular biology↗

bioRxiv2024

Platinum drug reprogramming of protein phosphorylation

Cisplatin is a DNA-targeting chemotherapeutic. Here we investigate how the cisplatin damaged gene loci are linked to specific protein-driven signalling pathways. Using forward chemical genetics methods, cisplatin damage to specific genes has been mapped in human lung cancer cells, a total of 16216 cisplatin-damaged genes (CDGs) with fold-enrichment > 1.5. Surprisingly, bioinformatics analysis demonstrates that cisplatin targets the majority of human protein kinase and phosphatase genes and involved in 300 core signalling pathways (-log p >4). The most associated key signalling pathways are sperm motility and protein kinase A. The highest related disease is cancer, and tissue toxicities related to CDGs are about hepato- and nephro- toxicities. Notably, cisplatin damaged 85% (440) of human protein kinase genes and 81% (110) of human protein phosphatase genes. This suggests that cisplatin acts as a multi-targeting protein-phosphorylation regulator, confirmed by a significant decrease in expression of a series of key protein kinase genes. These results reveal that cisplatin disrupts protein phosphorylation signalling genome-wide. Key points summarising the key messages of your articleA total of 16216 genes damaged by the clinical drug cisplatin in human lung cancer cells were identified using a forward chemical genetics method. Bioinformatics analysis revealed that cisplatin targets 85% of protein kinase and 81% of phosphatase genes. Cisplatin acts as a multi-targeting protein-phosphorylation regulator, disrupting protein phosphorylation signalling genome-wide. The disturbance of protein phosphorylation by cisplatin can be related to anticancer activity and tissue toxicities of the drug. These findings suggests novel strategies for rational design of next-generation anticancer metallodrugs involving specific targeting of protein phosphorylation.

cancer biology↗

bioRxiv2024

Multiomics analysis reveals B. MO1 as a distinct Babesia species and provides insights into its evolution and virulence.

Babesiosis, caused by protozoan parasites of the genus Babesia, is an emerging tick-borne disease of significance for both human and animal health. Babesia parasites infect erythrocytes of vertebrate hosts where they develop and multiply rapidly to cause the pathological symptoms associated with the disease. The identification of various Babesia species underscores the ongoing risk of new zoonotic pathogens capable of infecting humans, a concern amplified by anthropogenic activities and environmental shifts impacting the distribution and transmission dynamics of parasites, their vectors, and reservoir hosts. One such species, Babesia MO1, previously implicated in severe cases of human babesiosis in the midwestern United States, was initially considered closely related to B. divergens, the predominant agent of human babesiosis in Europe. Yet, uncertainties persist regarding whether these pathogens represent distinct variants of the same species or are entirely separate species. We show that although both B. MO1 and B. divergens share similar genome sizes, comprising three nuclear chromosomes, one linear mitochondrial chromosome, and one circular apicoplast chromosome, major differences exist in terms of genomic sequence divergence, gene functions, transcription profiles, replication rates and susceptibility to antiparasitic drugs. Furthermore, both pathogens have evolved distinct classes of multigene families, crucial for their pathogenicity and adaptation to specific mammalian hosts. Leveraging genomic information for B. MO1, B. divergens, and other members of the Babesiidae family within Apicomplexa provides valuable insights into the evolution, diversity, and virulence of these parasites. This knowledge serves as a critical tool in preemptively addressing the emergence and rapid transmission of more virulent strains.

microbiology↗

bioRxiv2023

Artificial intelligence-assisted stimulated Raman molecular cytology enables accurate and rapid intraoperative assessment of the surgical margin in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly mortal cancer whose only potentially curative treatment is surgical resection. Intraoperative assessment of its surgical margins is vital for patient survival. Frozen-section biopsy is routinely performed for this purpose. However, its high dependence on pathologists experience frequently poses diagnostic discrepancies. The essential invasiveness of PDAC also causes sampling errors. This study developed an intelligent molecular cytology approach with improved diagnostic objectivity and broader sampling coverage. Our method, Multi-Instance Cytology with Learned Raman Embedding (MICLEAR), is characterized by compositional information provided by label-free Raman imaging. First, 4085 cells were brushed off from the pancreases of 41 patients and imaged using stimulated Raman scattering microscopy. Then, a contrastive learning-based cell embedding model was developed to compress each cells morphological and compositional information into a compact cell vector. Finally, a multi-instance learning-based diagnosis model using cell vectors was employed to predict the likelihood of a patients margin being positive. MICLEAR achieved 80% sensitivity, 94.1% specificity, and an AUC of 0.86 on 27 patients for validation, comprising 10 with positive margins and 17 with negative ones, within approximately 8 minutes per patient. It may hold promises for more efficient and accurate intraoperative assessment of PDAC surgical margins.

biophysics↗

bioRxiv2023

Enhancing Hi-C contact matrices for loop detection with Capricorn, a multi-view diffusion model

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. On the other hand, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops, and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17.8% in mean squared error and 22.9% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 15.7% relative to existing methods. We further show that our multi-view idea can also be used to improve several existing methods, Hi-CARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

bioinformatics↗

bioRxiv2023

Probing Molecular Diversity and Ultrastructure of Brain Cells with Fluorescent Aptamers

Detergent-free immunolabeling has been proven feasible for correlated light and electron microscopy, but its application is restricted by the availability of suitable affinity reagents. Here we introduce CAptVE, a method using slow off-rate modified aptamers for cell fluorescence labeling on ultrastructurally reconstructable electron micrographs. CAptVE provides labeling for a wide range of biomarkers, offering a pathway to integrate molecular analysis into recent approaches to delineate neural circuits via connectomics.

neuroscience↗

bioRxiv2023

Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction

BackgroundProtein-protein interactions play essential roles in almost all biological processes. The binding interfaces between interacting proteins impose evolutionary constraints, leading to co-evolutionary signals that have successfully been employed to predict protein interactions from multiple sequence alignments (MSAs). During the construction of MSAs for this purpose, critical choices have to be made: how to ensure the reliable identification of orthologs, how to deal with paralogs, and how to optimally balance the need for large alignments versus sufficient alignment quality. ResultsHere, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed, each covering only a single clade in the tree of life. Co-evolutionary signals are searched separately within these clades, and are only subsequently integrated into a final interaction prediction using machine learning. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. ConclusionsGiven the recent successes of AlphaFold in predicting protein-protein interactions at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates - thus reducing false positives as well as computation time.

bioinformatics↗

bioRxiv2023

Tandemly duplicated MYB genes specifically in the Phaseoleae lineage are functionally diverged in the regulation of anthocyanin biosynthesis

Gene duplications have long been recognized as a driving force in the evolution of genes, giving rise to novel functions. The soybean genome is characterized by a large extent of duplicated genes. However, the extent and mechanisms of functional divergence among these duplicated genes in soybean remain poorly understood. In this study, we revealed that tandem duplication of MYB genes, which occurred specifically in the Phaseoleae lineage, exhibited a stronger purifying selection in soybean compared to common bean. To gain insights into the diverse functions of these MYB genes in anthocyanin biosynthesis, we examined the expression, transcriptional activity, metabolite, and evolutionary history of four MYB genes (GmMYBA5, GmMYBA2, GmMYBA1 and Glyma.09g235000), which were presumably generated by tandem duplication in soybean. Our data revealed that Glyma.09g235000 had become a pseudogene, while the remaining three MYB genes exhibited strong transcriptional activation activity and promoted anthocyanin biosynthesis in different soybean tissues. Furthermore, GmMYBA5 produced distinct compounds in Nicotiana benthamiana leaves compared to GmMYBA2 and GmMYBA1 due to variations in their DNA binding domains. The lower expression of anthocyanin related genes in GmMYBA5 resulted in lower levels of anthocyanins compared to GmMYBA2 and GmMYBA1. Metabolomics analysis further demonstrated the diverse and differential downstream metabolites, suggesting their functional divergence in metabolites following gene duplication. Together, our data provided evidence of functional divergence within the MYB gene cluster following tandem duplication, which shed light on the potential evolutionary direction of gene duplications during legume evolution.

evolutionary biology↗

bioRxiv2023

Multiplexed Volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

neuroscience↗