Microbiome modeling: a beginner's guide.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Unsupervised modeling of mutational landscapes of adeno-associated viruses viability.

Adeno-associated viruses 2 (AAV2) are minute viruses renowned for their capacity to infect human cells and akin organisms. They have recently emerged as prominent candidates in the field of gene therapy, primarily attributed to their inherent non-pathogenic nature in humans and the safety associated with their manipulation. The efficacy of AAV2 as gene therapy vectors hinges on their ability to infiltrate host cells, a phenomenon reliant on their competence to construct a capsid capable of breaching the nucleus of the target cell. To enhance their infection potential, researchers have extensively scrutinized various combinatorial libraries by introducing mutations into the capsid, aiming to boost their effectiveness. The emergence of high-throughput experimental techniques, like deep mutational scanning (DMS), has made it feasible to experimentally assess the fitness of these libraries for their intended purpose. Notably, machine learning is starting to demonstrate its potential in addressing predictions within the mutational landscape from sequence data. In this context, we introduce a biophysically-inspired model designed to predict the viability of genetic variants in DMS experiments. This model is tailored to a specific segment of the CAP region within AAV2's capsid protein. To evaluate its effectiveness, we conduct model training with diverse datasets, each tailored to explore different aspects of the mutational landscape influenced by the selection process. Our assessment of the biophysical model centers on two primary objectives: (i) providing quantitative forecasts for the log-selectivity of variants and (ii) deploying it as a binary classifier to categorize sequences into viable and non-viable classes.

Vaccine design and development: Exploring the interface with computational biology and AI.

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design.

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

Inspiring a convergent engineering approach to measure and model the tissue microenvironment.

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil.

COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.

Flock2: A model for orientation-based social flocking.

The aerial flocking of birds, or murmurations, has fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneously. To understand these behaviors a new model is presented for orientation-based social flocking. Previous methods model inter-bird dynamics by considering the neighborhood around each bird, and introducing forces for avoidance, alignment, and cohesion as three dimensional vectors that alter acceleration. Our method introduces orientation-based social flocking that treats social influences from neighbors more realistically as a desire to turn, indirectly controlling the heading in an aerodynamic model. While our model can be applied to any flocking social bird we simulate flocks of starlings, Sturnus vulgaris, and demonstrate the possibility of orientation waves in the absence of predators. Our model exhibits spherical and ovoidal flock shapes matching observation. Comparisons of our model to Reynolds' on energy consumption and frequency analysis demonstrates more realistic motions, significantly less energy use in turning, and a plausible mechanism for emergent orientation waves.

Combining single-gene-resistant and pyramided cultivars of perennial crops in agricultural landscapes compromises pyramiding benefits in most production situations.

While resistant cultivars are valuable in safeguarding crops against diseases, they can be rapidly overcome by pathogens. Numerous strategies have been proposed to delay pathogen adaptation (evolutionary control), while still ensuring effective protection (epidemiological control). For perennial crops, multiple resistance genes can be deployed 1) in the same cultivar (pyramiding strategy), in single-gene-resistant cultivars grown 2) in the same field (mixture strategy) or 3) in different fields (mosaic strategy), or 4) in hybrid strategies that combine the three previous options. In addition, the spatial scale at which resistant cultivars are deployed can affect the plant-pathogens interaction: small fields are thought to reduce pest density and disease transmission. Here we used the spatially-explicit stochastic model landsepi to compare the evolutionary and epidemiological control across spatial scales and deployment strategies relying on two major resistance genes. Our results, broadly focused on resistance to downy mildew of grapevine, show that the evolutionary control provided by the pyramiding strategy is at risk when single-gene-resistant cultivars are concurrently planted in the landscape (hybrid strategies), especially at low mutation probability. Moreover, the effectiveness of pyramiding compared to hybrid strategies is influenced by whether the adapted pathogen pays a fitness cost across all hosts or only for unnecessary virulence, particularly when the fitness cost is high rather than intermediate. Finally, field size did not affect model outputs for a wide range of mutation probabilities and associated fitness costs. The socio-economic policies favoring the adoption of optimal resistant management strategies are discussed.

Enrichment of rare codons at 5' ends of genes is a spandrel caused by evolutionary sequence turnover and does not improve translation.

Previously, Tuller et al. found that the first 30-50 codons of the genes of yeast and other eukaryotes are slightly enriched for rare codons. They argued that this slowed translation, and was adaptive because it queued ribosomes to prevent collisions. Today, the translational speeds of different codons are known, and indeed rare codons are translated slowly. We re-examined this 5' slow translation 'ramp.' We confirm that 5' regions are slightly enriched for rare codons; in addition, they are depleted for downstream Start codons (which are fast), with both effects contributing to slow 5' translation. However, we also find that the 5' (and 3') ends of yeast genes are poorly conserved in evolution, suggesting that they are unstable and turnover relatively rapidly. When a new 5' end forms de novo, it is likely to include codons that would otherwise be rare. Because evolution has had a relatively short time to select against these codons, 5' ends are typically slightly enriched for rare, slow codons. Opposite to the expectation of Tuller et al., we show by direct experiment that genes with slowly translated codons at the 5' end are expressed relatively poorly, and that substituting faster synonymous codons improves expression. Direct experiment shows that slow codons do not prevent downstream ribosome collisions. Further informatic studies suggest that for natural genes, slow 5' ends are correlated with poor gene expression, opposite to the expectation of Tuller et al. Thus, we conclude that slow 5' translation is a 'spandrel'--a non-adaptive consequence of something else, in this case, the turnover of 5' ends in evolution, and it does not improve translation.

In silico bioprospecting of receptors associated with the mechanism of action of Rondonin, an antifungal peptide from spider Acanthoscurria rondoniae haemolymph.

Multiple drug-resistant fungal species are associated with the development of diseases. Thus, more efficient drugs for the treatment of these aetiological agents are needed. Rondonin is a peptide isolated from the haemolymph of the spider Acanthoscurria rondoniae. Previous studies have shown that this peptide has antifungal activity against Candida sp. and Trichosporon sp. strains, acting on their genetic material. However, the molecular targets involved in its biological activity have not yet been described. Bioinformatics tools were used to determine the possible targets involved in the biological activity of Rondonin. The PharmMapper server was used to search for microorganismal targets of Rondonin. The PatchDock server was used to perform the molecular docking. UCSF Chimera software was used to evaluate these intermolecular interactions. In addition, the I-TASSER server was used to predict the target ligand sites. Then, these predictions were contrasted with the sites previously described in the literature. Molecular dynamics simulations were conducted for two promising complexes identified from the docking analysis. Rondonin demonstrated consistency with the ligand sites of the following targets: outer membrane proteins F (id: 1MPF) and A (id: 1QJP), which are responsible for facilitating the passage of small molecules through the plasma membrane; the subunit of the flavoprotein fumarate reductase (id: 1D4E), which is involved in the metabolism of nitrogenous bases; and the ATP-dependent Holliday DNA helicase junction (id: 1IN4), which is associated with histone proteins that package genetic material. Additionally, the molecular dynamics results indicated the stability of the interaction of Rondonin with 1MPF and 1IN4 during a 10 ns simulation. These interactions corroborate with previous in vitro studies on Rondonin, which acts on fungal genetic material without causing plasma membrane rupture. Therefore, the bioprospecting methods used in this research were considered satisfactory since they were consistent with previous results obtained via in vitro experimentation. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00224-1.

The growing repertoire of phage anti-defence systems.

The biological interplay between phages and bacteria has driven the evolution of phage anti-defence systems (ADSs), which evade bacterial defence mechanisms. These ADSs bind and inhibit host defence proteins, add covalent modifications and deactivate defence proteins, degrade or sequester signalling molecules utilised by host defence systems, synthesise and restore essential molecules depleted by bacterial defences, or add covalent modifications to phage molecules to avoid recognition. Overall, 145 phage ADSs have been characterised to date. These ADSs counteract 27 of the 152 different bacterial defence families, and we hypothesise that many more ADSs are yet to be discovered. We discuss high-throughput approaches (computational and experimental) which are indispensable for discovering new ADSs and the limitations of these approaches. A comprehensive characterisation of phage ADSs is critical for understanding phage-host interplay and developing clinical applications, such as treatment for multidrug-resistant bacterial infections.

GPT-4 as a biomedical simulator.

BACKGROUND: Computational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems. METHODS: We introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients. RESULTS: In qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival. CONCLUSION: This proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.

Deep Learning Based Barley Disease Quantification for Sustainable Crop Production.

Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley (Hordeum vulgare) plants and can result in significant crop losses. In this study, we developed a deep-learning model to quantify net blotch disease symptoms on different days post-infection on seedling leaves using Cascade R-CNN (Region-Based Convolutional Neural Networks) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4-days post infection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease.

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Understanding variants of unknown significance: the computational frontier.

The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Single-cell transcriptome analysis of cavernous tissues reveals the key roles of pericytes in diabetic erectile dysfunction.

Erectile dysfunction (ED) affects a significant proportion of men aged 40-70 and is caused by cavernous tissue dysfunction. Presently, the most common treatment for ED is phosphodiesterase 5 inhibitors; however, this is less effective in patients with severe vascular disease such as diabetic ED. Therefore, there is a need for development of new treatment, which requires a better understanding of the cavernous microenvironment and cell-cell communications under diabetic condition. Pericytes are vital in penile erection; however, their dysfunction due to diabetes remains unclear. In this study, we performed single-cell RNA sequencing to understand the cellular landscape of cavernous tissues and cell type-specific transcriptional changes in diabetic ED. We found a decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in diabetic fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. Moreover, the newly identified pericyte-specific marker, Limb Bud-Heart (Lbh), in mouse and human cavernous tissues, clearly distinguishing pericytes from smooth muscle cells. Cell-cell interaction analysis revealed that pericytes are involved in angiogenesis, adhesion, and migration by communicating with other cell types in the corpus cavernosum; however, these interactions were highly reduced under diabetic conditions. Lbh expression is low in diabetic pericytes, and overexpression of LBH prevents erectile function by regulating neurovascular regeneration. Furthermore, the LBH-interacting proteins (Crystallin Alpha B and Vimentin) were identified in mouse cavernous pericytes through LC-MS/MS analysis, indicating that their interactions were critical for maintaining pericyte function. Thus, our study reveals novel targets and insights into the pathogenesis of ED in patients with diabetes.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling.

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.