Proteomics stands as the crucial link between genomics and human diseases. Quantitative proteomics provides detailed insights into protein levels, enabling differentiation between distinct phenotypes. OLINK, a biotechnology company from Uppsala, Sweden, offers a targeted, affinity-based protein measurement method called Target 96, which has become prominent in the field of proteomics. The SCALLOP consortium, for instance, contains data from over 70.000 individuals across 45 independent cohort studies, all sampled by OLINK. However, when independent cohorts want to collaborate and quantitatively compare their target 96 protein values, it is currently advised to include 'identical biological bridging' samples in each sampling run to perform a reference sample normalization, correcting technical variations across measurements. Such a 'biological bridging sample' approach requires each of the involved cohorts to resend their biological bridging samples to OLINK to run them all together, which is logistically challenging, costly and time-consuming. Hence alternatives are searched and an evaluation of the current state of the art exposes the need for a more robust method that allows all OLINK Target 96 studies to compare proteomics data accurately and cost-efficiently. To meet these goals we developed the Synthetic Plasma Pool Cohort Correction, the 'SPOC correction' approach, based on the use of an OLINK-composed synthetic plasma sample. The method can easily be implemented in a federated data-sharing context which is illustrated on a sepsis use case.

The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.

MOTIVATION: Microscopy imaging is an essential tool for medical diagnosis and molecular biology. It is particularly useful for extracting information about disease states, tissue heterogeneity and cell specific parameters such as cell type or cell size from biological specimens. However, the information obtained from the images is likely to be subjected to sampling and observational bias with respect to the underlying cell size/type distributions. RESULTS: We present an algorithm, Estimate Tissue Cell Size/Type Distribution (EstiTiCS), for the adjustment of the underestimation of the number of small cells and the size of measured cells while accounting for the section thickness independent of the tissue type. We introduce the sources of bias under different tissue distributions and their effect on the measured values with simulation experiments. Furthermore, we demonstrate our method on histological sections of paraffin-embedded adipose tissue sample images from 57 people from a dietary intervention study. This data consists of measured cell size and its distribution over the dietary intervention period at four time points. Adjusting for the bias with EstiTiCS results in a closer fit to the true/expected adipocyte size distribution with earlier studies. Therefore, we conclude that our method is suitable as the final step in estimating the tissue wide cell type/size distribution from microscopy imaging pipeline. AVAILABILITY AND IMPLEMENTATION: Source code and its documentation are available at https://github.com/michaelLenz/EstiTiCS The whole pipeline of our method is implemented in R and makes use of the 'nloptr' package. Adipose tissue data used for this study are available on request. CONTACT: [email protected], [email protected].