Wuttke, D. | bioRxiv Science

bioRxiv2026

pyKinaXe: a fast and robust turnkey kinase activity profiler with high resolution

MotivationPeptide microarray technologies such as PamGenes enable direct measurement of peptide phosphorylation by upstream kinases, yet extraction of kinases from raw data depends on proprietary software or separate open-source alternatives delivering time-consuming processing across a variety of different steps, limiting throughput for experimental large-scale kinome generation in clinical and research settings. ResultsWe developed pyKinaXe, a Python package for automated end-to-end analysis of PamChip(R) data, integrating robust image processing, quantification of phosphorylation kinetics, multi-database substrate-kinase mapping, and upstream kinase analysis into a single one-click pipeline. Validation on a selected published benchmark dataset recovered 76-89% of the signaling pathways for previously reported significantly deregulated kinases. Processing time was reduced on the same data from over 30 minutes to[~] 25 seconds, leading to a 75-fold speed increase compared to other open-source alternatives. Thus, pyKinaXe addresses the key limitations of existing peptide-microarray-based kinase activity inference tools (slow inference, fragmented workflows, and poor usability) enabling fast and robust analysis, and facilitating high-throughput experiments and large-scale kinome profiling. Availability and implementationpyKinaXe is implemented in Python 3.13 and distributed under the Apache 2.0 License. Source code, documentation, and installation instructions are freely available at https://github.com/pykinaxe/pyKinaXe. The benchmark data is available at Mendeley Data (doi: 10.17632/ynp7f92n47.1). A pyKinaXes user-friendly web-based interface can be accessed at https://pykinaxe.github.io/home.

bioinformatics↗

bioRxiv2022

Deep learning based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra.

Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilized DLAP to analyse subtypes of tyrosine hydroxylase (TH)-positive dopaminergic midbrain neurons in mouse and human brain sections. These neurons modulate complex behaviour, and are differentially affected in Parkinsons and other diseases. DLAP allows the analysis of large cell numbers, and facilitates the identification of small cellular subpopulations. Specifically, we identified a small subpopulation of TH-positive neurons ([~]5%), mainly located in the very lateral Substantia nigra (SN), that was immunofluorescence-negative for the plasmalemma dopamine transporter (DAT), with [~]40% smaller cell bodies. These neurons were negative for aldehyde dehydrogenase 1A1, with a lower co-expression rate for dopamine-D2-autoreceptors, but a [~]7-fold higher likelihood of calbindin-d28k co-expression ([~]70%). Our results have important implications, as DAT is crucial for dopamine-signalling, and is commonly used as a marker for dopaminergic SN neurons.

neuroscience↗