Andi Bergeson

Diabetic kidney disease (DKD), the leading cause of kidney failure, is marked by clinical and molecular heterogeneity, making therapeutic development exceedingly difficult¹. Here we used Xenium and CosMx single-cell spatial transcriptomics, integrated with single-nucleus RNA sequencing, to build a cross-platform kidney atlas that makes tissue architecture computable for prognosis, non-invasive detection and patient selection. Using this atlas, we defined reproducible tissue niches and injury-linked microenvironments and uncovered a profibrotic context that expands with disease and tracks with worse kidney function. Within this architecture, we identified a B cell-predominant, tertiary lymphoid structure-like immune microenvironment that defines a distinct DKD subset with accelerated progression to renal end-points. We developed tissue biomarkers and a matched plasma protein panel that capture this biology, stratify patients in a population biobank and improve risk prediction beyond clinical models-supporting their potential for biomarker-guided selection in future B cell-targeted DKD trials.

Despite significant progress in characterizing neocortical cell types, a complete understanding of the synaptic connections of individual excitatory cells remains elusive. This study investigates the connectivity of mouse visual cortex thick tufted layer 5 pyramidal cells, also known as extratelencephalic neurons (L5-ETns), using a 1 mm³ publicly available electron microscopy dataset. The analysis reveals that, in their immediate vicinity, L5-ETns primarily establish connections with a group of inhibitory cell types, which, in turn, specifically target the L5-ETns back. The most common excitatory targets of L5-ETns are layer 5 intertelencephalic neurons (L5-ITns) and layer 6 (L6) pyramidal cells, whereas synapses with other L5-ETns are less common. When L5-ETns extend their axons to other cortical regions, they tend to connect more with excitatory cells. Our results highlight a circuit motif where a subclass of excitatory cells forms a subcircuit with specific inhibitory cell types. This is achieved using a publicly available, automated approach for synapse recognition and automated cell typing, offering a framework for exploring the connectivity of other neuron types.

The neocortex is composed of microcircuits built from distinct cell types. Despite significant progress in characterizing these cell types, understanding the full synaptic connections of individual excitatory cells remains elusive. This study investigates the connectivity of arguably the most well recognized excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell also known as extra telencephalic (ET) neurons using a 1 mm3 publicly available electron microscopy dataset. The analysis reveals that in their immediate vicinity ET neurons primarily establish recurrent connections with a specific group of inhibitory cell types. The most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. When ET cells extend their axons to other cortical regions, they tend to connect more with excitatory cells. Our results highlight a specific circuit pattern of a L5ET neuron using an automated approach for synapse recognition and automated cell typing, which is publicly available offering a framework for exploring the connectivity of other neuron types.