Frequent longitudinal blood microsampling and proteome monitoring identify disease markers and enable timely intervention in a mouse model of type 1 diabetes - Parajuli, Anirudra; Bendes, Annika; Byvald, Fabian; Stone, Virginia M.; Ringqvist, Emma; Butrym, Marta; Angelis, Emmanouil; Kipper, Sophie; Bauer, Stefan; Roxhed, Niclas; Schwenk, Jochen; Flodström Tullberg, Malin (Karolinska Institutet, 2025)
The work has been published as Parajuli et al. (2025) Diabetologia (https://doi.org/10.1007/s00125-025-06502-7) Frequent self-sampling of blood has the potential to identify early, disease-predictive markers, including proteins. In a study to test this hypothesis, we conducted regular microsampling of a mouse model over 14 days and monitored their molecular response to a type 1 diabetes (T1D)-associated virus.

This longitudinal approach involved the collection of dried blood samples, which were subsequently analysed for 92 circulating proteins. The data revealed transient molecular changes in the virus-infected mice. Utilising machine learning techniques, we achieved a prediction accuracy of over 90% for infection status after day 2 post-infection. This high level of accuracy enabled timely treatment interventions with immune serum, which could potentially prevent the onset of diabetes in the infected animals.

The data of this study underscores the utility of frequent blood microsampling as a method for monitoring disease progression during the pre-symptomatic phase, allowing for prompt medical interventions of immune-mediated inflammatory diseases, including T1D.

Description of data files:- Mouse DBS_Batch 1_ProtPQN data_rmd1021.csv: ProtPQN normalised NPX values for Study batch 1. The signals are in log2-scale. All columns not described below contain protein measurements.
- Mouse DBS_Batch 2_ProtPQN data.csv: ProtPQN normalised NPX values for Study batch 2. The signals are in log2-scale. All columns not described below contain protein measurements.
- zscore_data_combined.csv: Z-score transformed measurements for both studies. All columns not described below contain protein measurements.
- sample information study batch 1 and 2.csv: Sample information for all of the mice.
- Mouse DBS_NPX_below LOD.xlsx; Raw data from Olink Signature NPX software for Study batch 1. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein.
- Mouse DBS Plate 2_NPX_belowLOD.xlsx: Raw data from Olink Signature NPX software for Study batch 2. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein
Keywords: Proteomics and intermolecular interactions (excl. medical proteomics); Systems biology; Endocrinology; Infectious agents; Machine learning not elsewhere classified; Biomedical engineering not elsewhere classified; Medical and Health Sciences; Medicin och hälsovetenskap; Biological Sciences; Biologi