Genetically defined causal effects of natural killer cells related traits in risk of infection: a Mendelian randomization study.

BACKGROUND: The intricate interplay between genetics and immunology often dictates the host's susceptibility to various diseases. This study explored the genetic causal relationship between natural killer (NK) cell-related traits and the risk of infection. METHODS: Single-nucleotide polymorphisms (SNPs) significantly associated with NK cell-related traits were selected as instrumental variables to estimate their genetic causal effects on infection. SNPs from a genome-wide association study (GWAS) on NK cell-related traits, including absolute cell counts, median fluorescence intensities reflecting surface antigen levels, and relative cell counts, were used as exposure instruments. Summary-level GWAS statistics of four phenotypes of infection were used as the outcome data. The exposure and outcome data were analyzed via the two-sample Mendelian randomization method. RESULTS: Each one standard deviation increase in the expression level of human leukocyte antigen (HLA)-DR on HLA-DR+ NK cells was associated with a lower risk of pneumonia (P < 0.05). An increased HLA-DR+ NK/CD3- lymphocyte ratio was related to a lower of risk of pneumonia (P  < 0.05). Each one standard deviation increase in the absolute count of HLA-DR+ NK cells was associated with a lower risk of both bacterial pneumonia and pneumonia (P < 0.05). An increased HLA-DR+ NK/NK ratio was associated with a decreased risk of both pneumonia and bacterial pneumonia (P < 0.05). The results were robust under all sensitivity analyses. No evidence for heterogeneity, pleiotropy, or potential reverse causality was detected. Notably, our analysis did not reveal any significant associations between NK cell-related traits and other phenotypes of infection, including cellulitis, cystitis, and intestinal infection. CONCLUSIONS: HLA-DR+ NK cells could be a novel immune cell trait associated with a lower risk of bacterial pneumonia or pneumonia.

Leveraging single-cell RNA-seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping.

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag-RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping.

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag-RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

Single-cell RNA sequencing reveals melanoma cell state-dependent heterogeneity of response to MAPK inhibitors.

BACKGROUND: Melanoma is a heterogeneous cancer influenced by the plasticity of melanoma cells and their dynamic adaptations to microenvironmental cues. Melanoma cells transition between well-defined transcriptional cell states that impact treatment response and resistance. METHODS: In this study, we applied single-cell RNA sequencing to interrogate the molecular features of immunotherapy-naive and immunotherapy-resistant melanoma tumours in response to ex vivo BRAF/MEK inhibitor treatment. FINDINGS: We confirm the presence of four distinct melanoma cell states - melanocytic, transitory, neural-crest like and undifferentiated, and identify enrichment of neural crest-like and undifferentiated melanoma cells in immunotherapy-resistant tumours. Furthermore, we introduce an integrated computational approach to identify subsets of responding and nonresponding melanoma cells within the transcriptional cell states. INTERPRETATION: Nonresponding melanoma cells are identified in all transcriptional cell states and are predisposed to BRAF/MEK inhibitor resistance due to pro-inflammatory IL6 and TNFɑ signalling. Our study provides a framework to study treatment response within distinct melanoma cell states and indicate that tumour-intrinsic pro-inflammatory signalling contributes to BRAF/MEK inhibitor resistance. FUNDING: This work was supported by Macquarie University, Melanoma Institute Australia, and the National Health and Medical Research Council of Australia (NHMRC; grant 2012860, 2028055).

SANTO: a coarse-to-fine alignment and stitching method for spatial omics.

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Antecedent Cardiac Arrest Status of Donation After Circulatory Determination of Death (DCDD) Kidney Donors and the Risk of Delayed Graft Function After Kidney Transplantation: A Cohort Study.

BACKGROUND: The number of donors from donation after circulatory determination of death (DCDD) has increased by at least 4-fold over the past decade. This study evaluated the association between the antecedent cardiac arrest status of controlled DCDD donors and the risk of delayed graft function (DGF). METHODS: Using data from the Australia and New Zealand Dialysis and Transplant, the associations between antecedent cardiac arrest status of DCDD donors before withdrawal of cardiorespiratory support, DGF, posttransplant estimated glomerular filtration rate (eGFR), and allograft loss were examined using adjusted logistic, linear mixed modeling, and cox regression, respectively. Among donors who experienced cardiac arrest, we evaluated the association between duration and unwitnessed status of arrest and DGF. RESULTS: A total of 1173 kidney transplant recipients received DCDD kidneys from 646 donors in Australia between 2014 and 2019. Of these, 335 DCDD had antecedent cardiac arrest. Compared with recipients of kidneys from donors without antecedent cardiac arrest, the adjusted odds ratio (95% confidence interval) for DGF was 0.85 (0.65-1.11) among those with kidneys from donors with cardiac arrest. There was no association between antecedent cardiac arrest and posttransplant eGFR or allograft loss. The duration of cardiac arrest and unwitnessed status were not associated with DGF. CONCLUSIONS: This focused analysis in an Australian population showed that the allograft outcomes were similar whether DCDD donors had experienced a prior cardiac arrest, with no associations between duration or unwitnessed status of arrest and risk of DGF. This study thus provides important reassurance to transplant programs and the patients they counsel, to accept kidneys from donors through the DCDD pathway irrespective of a prior cardiac arrest.

Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study.

Background: The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery. Methods: Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved. Conclusions: FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.

Severe Jarisch-Herxheimer Reaction (JHR) in a leptospirosis patient: A case report.

Leptospirosis is a zoonosis that is related to potential respiratory, renal, neurological, and cardiovascular failure. At present, antibiotics are the recommended treatment, but due to the underlying cause of the disease, they may induce the Jarisch-Herxheimer reaction (JHR) within 24 hours. At the same time, we speculate that JHR may aggravate the natural course of leptospirosis. Considering that there are few available reports on this event, we will share a case of pulmonary hemorrhagic leptospirosis, where antibiotic treatment is suspected to have triggered the JHR. This report is expected to improve clinical attention to the relationship between leptospirosis and JHR.

Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE.

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

Pcdh19 mediates olfactory sensory neuron coalescence during postnatal stages and regeneration.

The mouse olfactory system regenerates constantly throughout life. While genes critical for the initial projection of olfactory sensory neurons (OSNs) to the olfactory bulb have been identified, what genes are important for maintaining the olfactory map during regeneration are still unknown. Here we show a mutation in Protocadherin 19 (Pcdh19), a cell adhesion molecule and member of the cadherin superfamily, leads to defects in OSN coalescence during regeneration. Surprisingly, lateral glomeruli were more affected and males in particular showed a more severe phenotype. Single cell analysis unexpectedly showed OSNs expressing the MOR28 odorant receptor could be subdivided into two major clusters. We showed that at least one protocadherin is differentially expressed between OSNs coalescing on the medial and lateral glomeruli. Moreover, females expressed a slightly different complement of genes from males. These features may explain the differential effects of mutating Pcdh19 on medial and lateral glomeruli in males and females.

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.

Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.

simKAP: simulation framework for the kidney allocation process with decision making model.

Organ shortage is a major barrier in transplantation and rules guarding organ allocation decisions should be robust, transparent, ethical and fair. Whilst numerous allocation strategies have been proposed, it is often unrealistic to evaluate all of them in real-life settings. Hence, the capability of conducting simulations prior to deployment is important. Here, we developed a kidney allocation simulation framework (simKAP) that aims to evaluate the allocation process and the complex clinical decision-making process of organ acceptance in kidney transplantation. Our findings have shown that incorporation of both the clinical decision-making and a dynamic wait-listing process resulted in the best agreement between the actual and simulated data in almost all scenarios. Additionally, several hypothetical risk-based allocation strategies were generated, and we found that these strategies improved recipients' long-term post-transplant patient survival and reduced wait time for transplantation. The importance of simKAP lies in its ability for policymakers in any transplant community to evaluate any proposed allocation algorithm using in-silico simulation.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2.

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Trajectories of systolic blood pressure decline in kidney transplant donors prior to circulatory death and delayed graft function.

Background: Kidneys donated after circulatory death suffer a period of functional warm ischaemia before death, which may lead to early ischaemic injury. Effects of haemodynamic trajectories during the agonal phase on delayed graft function (DGF) is unknown. We aimed to predict the risk of DGF using patterns of trajectories of systolic blood pressure (SBP) declines in Maastricht category 3 kidney donors. Methods: We conducted a cohort study of all kidney transplant recipients in Australia who received kidneys from donation after circulatory death donors, divided into a derivation cohort (transplants between 9 April 2014 and 2 January 2018 [462 donors]) and a validation cohort (transplants between 6 January 2018 and 24 December 2019 [324 donors]). Patterns of SBP decline using latent class models were evaluated against the odds of DGF using a two-stage linear mixed effects model. Results: In the derivation cohort, 462 donors were included in the latent class analyses and 379 donors in the mixed effects model. Of the 696 eligible transplant recipients, 380 (54.6%) experienced DGF. Ten different trajectories, with distinct patterns of SBP decline were identified. Compared with recipients from donors with the slowest decline in SBP after withdrawal of cardiorespiratory support, the adjusted odds ratio (aOR) for DGF was 5.5 [95% confidence interval (CI) 1.38-28.0] for recipients from donors with a steeper decline and lowest SBP [mean 49.5 mmHg (standard deviation 12.5)] at the time of withdrawal. For every 1 mmHg/min reduction in the rate of decline of SBP, the respective aORs for DGF were 0.95 (95% CI 0.91-0.99) and 0.98 (95% CI 0.93-1.0) in the random forest and least absolute shrinkage and selection operator models. In the validation cohort, the respective aORs were 0.95 (95% CI 0.91-1.0) and 0.99 (95% CI 0.94-1.0). Conclusion: Trajectories of SBP decline and their determinants are predictive of DGF. These results support a trajectory-based assessment of haemodynamic changes in donors after circulatory death during the agonal phase for donor suitability and post-transplant outcomes.

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data.

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Expanding Extender Substrate Selection for Unnatural Polyketide Biosynthesis by Acyltransferase Domain Exchange within a Modular Polyketide Synthase.

Modular polyketide synthases (PKSs) are polymerases that employ α-carboxyacyl-CoAs as extender substrates. This enzyme family contains several catalytic modules, where each module is responsible for a single round of polyketide chain extension. Although PKS modules typically use malonyl-CoA or methylmalonyl-CoA for chain elongation, many other malonyl-CoA analogues are used to diversify polyketide structures in nature. Previously, we developed a method to alter an extension substrate of a given module by exchanging an acyltransferase (AT) domain while maintaining protein folding. Here, we report in vitro polyketide biosynthesis by 13 PKSs (the wild-type PKS and 12 AT-exchanged PKSs with unusual ATs) and 14 extender substrates. Our ∼200 in vitro reactions resulted in 13 structurally different polyketides, including several polyketides that have not been reported. In some cases, AT-exchanged PKSs produced target polyketides by >100-fold compared to the wild-type PKS. These data also indicate that most unusual AT domains do not incorporate malonyl-CoA and methylmalonyl-CoA but incorporate various rare extender substrates that are equal to in size or slightly larger than natural substrates. We developed a computational workflow to predict the approximate AT substrate range based on active site volumes to support the selection of ATs. These results greatly enhance our understanding of rare AT domains and demonstrate the benefit of using the proposed PKS engineering strategy to produce novel chemicals in vitro.

Survival and clinicopathological significance of blood vessel invasion in operable breast cancer: a systematic review and meta-analysis.

BACKGROUND: Lymphovascular invasion, including lymphatic-vessel invasion and blood-vessel invasion, plays an important role in distant metastases. The metastatic pattern of blood-vessel invasion may differ from that of lymphatic-vessel invasion. However, its prognostic significance in breast cancer remains controversial. We evaluated the role of blood-vessel invasion in the prognosis of operable breast-cancer patients and its association with clinicopathological characteristics. METHODS: We systematically searched EMBASE, PubMed, the Cochrane Library and Web of Science for studies in English through December 2020. Disease-free survival, overall survival and cancer-specific survival were the primary outcomes. Pooled hazard ratios and 95% confidence intervals were assessed using a random-effects model. RESULTS: Twenty-seven studies involving 7954 patients were included. Blood-vessel invasion occurred in 20.4% of tumor samples. Pooled results showed significant associations of blood-vessel invasion with worse disease-free survival (hazard ratio = 1.82; 95% confidence interval = 1.43-2.31) and overall survival (hazard ratio = 1.86; 95% confidence interval = 1.16-2.99) in multivariate analyses. The results of the univariate analyses were similar. Among the clinicopathological factors, blood-vessel invasion was associated with larger tumor size, lymph-node metastasis, nonspecific invasive type, higher histological grade, estrogen receptor-negative breast cancer, human epidermal growth factor receptor 2-positive breast cancer and lymphatic-vessel invasion. In the lymph-node-negative subgroup analyses, the presence of blood-vessel invasion led to poorer disease-free survival (hazard ratio = 2.46; 95%confidence interval = 1.64-3.70) and overall survival (hazard ratio = 2.94; 95%confidence interval = 1.80-4.80). CONCLUSIONS: We concluded that blood-vessel invasion is an independent predictor of poor prognosis in operable breast cancer and is associated with aggressive clinicopathological features. Breast-cancer patients with blood-vessel invasion require more aggressive treatments after surgery.

Differential developmental blueprints of organ-intrinsic nervous systems.

The organ-intrinsic nervous system is a major interface between visceral organs and the brain, mediating important sensory and regulatory functions in the body-brain axis and serving as critical local processors for organ homeostasis. Molecularly, anatomically, and functionally, organ-intrinsic neurons are highly specialized for their host organs. However, the underlying mechanism that drives this specialization is largely unknown. Here, we describe the differential strategies utilized to achieve organ-specific organization between the enteric nervous system (ENS) 1 and the intrinsic cardiac nervous system (ICNS) 2 , a neuronal network essential for heart performance but poorly characterized. Integrating high-resolution whole-embryo imaging, single-cell genomics, spatial transcriptomics, proteomics, and bioinformatics, we uncover that unlike the ENS which is highly mobile and colonizes the entire gastrointestinal (GI) tract, the ICNS uses a rich set of extracellular matrix (ECM) genes that match with surrounding heart cells and an intermediate dedicated neuronal progenitor state to stabilize itself for a 'beads-on-the-necklace' organization on heart atria. While ICNS- and ENS-precursors are genetically similar, their differentiation paths are influenced by their host-organs, leading to distinct mature neuron types. Co-culturing ENS-precursors with heart cells shifts their identity towards the ICNS and induces the expression of heart-matching ECM genes. Our cross-organ study thus reveals fundamental principles for the maturation and specialization of organ-intrinsic neurons.