Biology subjects

Pineda, J.

Publications and source records attributed to Pineda, J..

3 recordsLinked to original sources

bioRxiv2026

Protein-guided RNA barcoding links transcriptomes to synaptic architecture

Mammalian brain function relies on the precise synaptic architecture of diverse cell types, yet scalable methods for linking a neurons transcriptomic profile to its neuroanatomy remain limited. We present Synapse-seq, an in vivo strategy in which cell-identifying barcoded mRNAs are routed to subcellular compartments via targeting proteins and detected by single-cell and spatial genomics. Using AAV delivery for minimal perturbation of gene expression, we directed barcodes to presynaptic terminals (via synaptophysin) in four distinct circuits, or to postsynaptic sites (via nanobodies to endogenous PSD95) of hippocampal excitatory neurons. In the mouse primary visual cortex, presynaptic Synapse-seq recovered known long-range projections and discovered cortical layer subtypes with distinct thalamic innervation. In the anterior cortex, we elucidated simple topographic rules of corticostriatal innervation: intratelencephalic neurons followed a continuous depth-to-target gradient, while extratelencephalic neurons exhibited striatal collaterals that spatially correlated with medullary innervation. Finally, postsynaptic barcoding of excitatory neurons revealed cell type-specific variation in dendritic architectures across and within hippocampal subfields. These data establish Synapse-seq as a versatile, genomics-based approach for the integrated definition of molecular identity and synaptic organization across mammalian brains.

neuroscience↗

bioRxiv2024

Cassini: Streamlined and Scalable Method for in situ profiling of RNA and Protein

In the expanding field of spatial genomics, numerous methods have emerged to decode biomolecules in intact tissue. Advanced techniques based on combinatorial decoding can resolve thousands of features in a reasonable time but are often constrained by either the prohibitive costs associated with commercial platforms or the complexity of developing custom instruments. Alternatively, sequential detection methods, like single-molecule FISH, are easier to implement but offer limited multiplexing capability or signal amplification. Here, we introduce Cassini, a new approach for straightforward, cost-effective multiplexed measurements of mRNA and protein features simultaneously. Cassini leverages rolling circle amplification (RCA), known for its robust amplification and remarkable stability even after intense stripping, to serially detect each feature in under 20 minutes of total experimental time. The method also enables simultaneous immunostaining with either fluorophore-conjugated or DNA-barcoded antibodies, through an optimized immunostaining buffer. In a single overnight run, we show that Cassini can quantify 32 features (comprising both RNA and proteins) with sensitivity similar to state-of-the-art FISH techniques. We provide a comprehensive protocol alongside an online probe-design platform (cassini.me), aiming to enhance accessibility and user-friendliness. With our open-source solution, we aspire to empower researchers to uncover the nuances of spatial gene expression dynamics across diverse biological landscapes.

bioengineering↗

bioRxiv2022

Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning

Chemical live/dead assay has a long history of providing information about the viability of cells cultured in vitro. The standard methods rely on imaging chemically-stained cells using fluorescence microscopy and further analysis of the obtained images to retrieve the proportion of living cells in the sample. However, such a technique is not only time-consuming but also invasive. Due to the toxicity of chemical dyes, once a sample is stained, it is discarded, meaning that longitudinal studies are impossible using this approach. Further, information about when cells start programmed cell death (apoptosis) is more relevant for dynamic studies. Here, we present an alternative method where cell images from phase-contrast time-lapse microscopy are virtually-stained using deep learning. In this study, human endothelial cells are stained live or apoptotic and subsequently counted using the self-supervised single-shot deep-learning technique (LodeSTAR). Our approach is less labour-intensive than traditional chemical staining procedures and provides dynamic live/apoptotic cell ratios from a continuous cell population with minimal impact. Further, it can be used to extract data from dense cell samples, where manual counting is unfeasible.

biophysics↗