Led by @robfal.bsky.social and Sergio A. Gomez-Ochoa, supervised by @juliosaezrod.bsky.social with Matthias Kretzler and Michael T Eadon. Thanks to all involved, including Charlotte Boys, @ricoramirez.bsky.social, @tanevski.bsky.social, and many more. We thank all funders and patient participants!
A step toward molecularly grounded, precision assessment of kidney injury and the possibility of non-invasive tracking of tissue damage using blood and urine biomarkers.
🩸🥃 We showed that urine and plasma proteomics reflects underlying injury, and plasma proteins were predictive of future CKD and AKI in an external cohort. Thinking of kidney disease as a continuum of damage rather than discrete categories could guide future biomarker discoveries.
🔄 Previously, we showed that epithelial injury is common to glomerular diseases. Here we find similar patterns in diabetic, hypertensive and acute kidney disease, supporting a unified view of chronic kidney disease irrespective of initiating cause.
doi.org/10.1101/2025...
🔎 We identified shared cellular and molecular programs of acute and chronic injury that cut across both AKI and CKD – moving beyond clinical labels. Instead, they aligned with eGFR and histopathologic features like tubular atrophy and fibrosis.
❓Recent studies showed the cellular diversity of the kidney in health and disease, but they rarely go back to see how the cellular landscape might shape disease in individuals who experience very heterogeneous clinical trajectories
🔗 Preprint: doi.org/10.64898/202...
🧵 What does single-cell transcriptomics reveal about individual patients with acute (AKI) and chronic (CKD) kidney 🫘 disease? Can we use it to find new biomarkers? In our new preprint 👇, we asked this using biopsies from 150+ participants from the Kidney Precision Medicine Project.
This study was led by @l-kuechenhoff.bsky.social, with supervision by @juliosaezrod.bsky.social and @ricoramirez.bsky.social & contributions from Gahyun Kim, @jlanzer.bsky.social, and Matthias Kretzler.
We acknowledge funding from the German Federal Ministry of Research, Technology, and Space.
We further combine single-cell data with spatial transcriptomics to identify spatially constrained fibrotic cell signalling events shared across organs. All results are published as an open resource to support further discovery: organfibrosis.saezlab.org
With our multi-level analysis, we first identify shared cell type-specific fibrotic programs within each organ. Then, we assess which parts of these gene programs are also shared across organs.
In this study, we take a cross-organ view by integrating single-cell transcriptomic data from over 5 million cells across 20 studies, covering fibrotic diseases of the heart, liver, kidney, and lung. 🫁🫀
Fibrosis affects nearly every organ and represents a major global health burden, yet it’s still mostly studied in organ- or disease-specific contexts. This makes it difficult to understand what mechanisms are truly shared across diseases and which are tissue-specific.
🧵 See 👇 our new preprint on shared and organ-specific gene expression programs of fibrotic diseases 🧬
📄 Paper: doi.org/10.64898/202...
📊 Explore the data: organfibrosis.saezlab.org
This study was led by @l-kuechenhoff.bsky.social, with supervision by @juliosaezrod.bsky.social and @ricoramirez.bsky.social & contributions from Gahyun Kim, @jlanzer.bsky.social, and Matthias Kretzler.
We acknowledge funding from the German Federal Ministry of Research, Technology, and Space.
We further combine single-cell data with spatial transcriptomics to identify spatially constrained fibrotic cell signalling events shared across organs. All results are published as an open resource to support further discovery: organfibrosis.saezlab.org
With our multi-level analysis, we first identify shared cell type-specific fibrotic programs within each organ. Then, we assess which parts of these gene programs are also shared across organs.
In this study, we take a cross-organ view by integrating single-cell transcriptomic data from over 5 million cells across 20 studies, covering fibrotic diseases of the heart, liver, kidney, and lung. 🫁🫀
Fibrosis affects nearly every organ and represents a major global health burden, yet it’s still mostly studied in organ- or disease-specific contexts. This makes it difficult to understand what mechanisms are truly shared across diseases and which are tissue-specific.
Led by @martingarridorc.bsky.social and Clement Potel with the help of @miraburtscher.bsky.social, Isabelle Becher, @pablormier.bsky.social, @smuellerdott.bsky.social. Supervised by Mikhail Savitski and @juliosaezrod.bsky.social. With support from @aihcluster.bsky.social and @embl.org facilities.
Original preprint: doi.org/10.1101/2024... Want more details on the data, prior knowledge, methods, and ground-truth sets?
Everything is in the paper and the accompanying Zenodo resource: zenodo.org/records/1839...
Why does this matter? This work challenges the traditional, largely linear view of signaling pathways and proposes a more complex, systematic, and data-driven framework for studying signaling at scale.
The result? While some combinations (e.g. literature and diffusion methods) recovered ~20% of known interactions, over 90% of interactions were missing from ground-truth sets, pointing to many potentially novel connections.
Using these data, kinase–substrate interactions, and network inference methods, we tested how well different resources recover known (ground-truth) signaling interactions between kinases.
We combined published phosphoproteomics data with three newly generated datasets to create the most comprehensive resource to date on the EGF signaling response, comprising more than 45,000 high-confidence phosphosite identifications
In this manuscript, we adopt the perspective of a naive researcher with the goal of learning the EGF signaling pathway — specifically kinase–kinase interactions — using phosphoproteomics and prior knowledge
Most of these pathways were built from small-scale, perturbation-based biochemical experiments. But how well can we recover known signaling pathways using a systematic, data-driven approach without perturbations?
What is a signaling pathway? For decades, researchers have used conceptual pathway maps to explain how cells transmit information. These maps have also been used to interpret molecular data and guide drug development efforts.
Interested in kinase-driven signaling interactions? Check out our (now peer-reviewed) paper together with @savitski-lab.bsky.social on reconstructing signaling networks from phosphoproteomics data and prior knowledge:
➡️ doi.org/10.1038/s414...
This month we welcomed Francisca Gaspar Vieira, PhD student from @brittavelten.bsky.social's lab, to visit us at @ebi.embl.org for 6 months 🎉
She will continue to develop probabilistic factor models explicitly incorporating uncertainty in the estimation of perturbation effects in single-cell data