RESUMO
Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the functional properties of transcriptionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.
RESUMO
Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.
RESUMO
Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.
