Using human iPSC-derived kidney organoids to decipher SARS-CoV-2 pathology on single cell level.

We describe a protocol for single-cell RNA sequencing of SARS-CoV-2-infected human induced pluripotent stem cell (iPSC)-derived kidney organoids. After inoculation of kidney organoids with virus, we use mechanical and enzymatic disruption to obtain single cell suspensions. Next, we process the organoid-derived cells into sequencing-ready SARS-CoV-2-targeted libraries.

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids.

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2.

PySpacell: A Python Package for Spatial Analysis of Cell Images.

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations.