Light microscopy based approach for mapping connectivity with molecular specificity

Abstract

Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy.

nGauge: Integrated and extensible neuron morphology analysis in Python

“…Combined with visualization tools, nGauge empowers the creation of publication-quality figures with ease. In fact, during the development of the nGauge project, we have already applied all of the individual modules to produce results both in publication and in preparation, finding it to be a very effective toolkit for efficient data science (Shen et al, 2020;Li et al, 2020;Dizaji et al, 2020;Duan et al, 2020).…”

“…Recent technological advances have enabled huge increases in the number of neuron reconstructions that can be performed in a single study into the hundreds (Gouwens et al, 2019(Gouwens et al, , 2020Jiang et al, 2021;BRAIN Initiative Cell Census Network (BICCN) et al, 2020). Multispectral labeling (Shen et al, 2020;Li et al, 2020) and large-volumetric electron microscopy (Motta et al, 2019;Yin et al, 2020;Phelps et al, 2021), in principle, allow reconstructing many more neurons within one brain or within a single common coordinate system (Wang et al, 2020). As a result, analysis techniques must be developed which allow these data to be integrated, with specific focuses on the ability to customize, automate and quickly expand processing workflows to handle large batches of individual neurons, including those reconstructed from various methods.…”

Nanoscale resolution imaging of the whole mouse embryos and larval zebrafish using expansion microscopy

“…The dense labeling of ATTO 647N NHS ester and Cy3 NHS ester enabled the registration of images acquired via repetitive staining and imaging with less than a 10-nm registration error (see Supplementary Note 4 for detailed procedure). Recently developed multiplexed tissue imaging techniques based on tissue expansion rely on repetitive staining and imaging, which require registration between images acquired at different imaging rounds 20,45,46 . DAPI-stained nuclei have been widely used as fiducial markers 45,46 .…”

“…Recently developed multiplexed tissue imaging techniques based on tissue expansion rely on repetitive staining and imaging, which require registration between images acquired at different imaging rounds 20,45,46 . DAPI-stained nuclei have been widely used as fiducial markers 45,46 . Here, we found that registration using DAPI as a fiducial marker yielded a registration error larger than 50 nm, especially when the density of the nucleus was low (Supplementary Fig.…”

A Picture Worth a Thousand Molecules—Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

“…Multi-round IHC has also been used with SMLM methods (Tam et al, 2014;Yi et al, 2016) and has been automated to increase experimental throughput (Klevanski et al, 2020). Multi-round IHC is also compatible with ExM to achieve multiplexed synaptic and neuronal imaging (Ku et al, 2016;Shen et al, 2020).…”