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Electronic devicesforrecording neuralactivityinthe nervoussyste m needto bescalableacrosslargespatialandte mporalscales whilealso providing millisecondandsingle-cellspatiote mporalresolution. H o w e v e r, e xi s ti n g hi g h- r e s ol u ti o n n e u r al r e c o r di n g d e vi c e s c a n n o t achievesi multaneousscalability on bothspatialandte mporallevels due toatrade-offbetweensensordensityand mechanicalflexibility. Here weintroduceathree-di mensional(3D)stackingi mplantableelectronic platfor m,basedonperfluorinateddielectricelasto mersandtissue-levelsoft multilayerelectrodes,thatenablesspatiote mporallyscalablesingle-cell neuralelectrophysiologyinthenervoussyste m. Ourelasto mersexhibit stable dielectric perfor mancefor overayearin physiologicalsolutions andare10,000ti messofterthanconventional plastic dielectrics. By leveragingthese uniquecharacteristics we developthe packaging of lithographednano metre-thickelectrodearraysina3Dconfiguration with across-sectionaldensityof7.6electrodesper100μ m2.Theresulting3D integrated multilayersoftelectrodearrayretainstissue-levelflexibility, reducingchronici m muneresponsesin mouse neuraltissues,and de monstratestheabilitytoreliablytrackelectricalactivityinthe mouse brain orspinalcord over months without disruptingani mal behaviour. 

Abstract Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.