Emma Lundin

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation pl...

Cancer is a highly heterogeneous disease in need of accurate and non-invasive diagnostic tools. Here, we describe a novel strategy to explore the proteome signature by comprehensive analysis of protein levels using a pan-cancer approach of patients representing the major cancer types. Plasma profiles of 1,463 proteins from more than 1,400 cancer patients representing altogether 12 common cancer types were measured in minute amounts of blood plasma collected at the time of diagnosis and before treatment. AI-based disease prediction models allowed for the identification of a set of proteins associated with each of the analyzed cancers. By combining the results from all cancer types, a panel of proteins suitable for the identification of all individual cancer types was defined. The results are presented in a new open access Human Disease Blood Atlas. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Flow cytometry is a powerful method for quantitative and qualitative analysis of individual cells. However, flow cytometric analysis of extracellular vesicles (EVs), and the proteins present on their surfaces has been hampered by the small size of the EVs - in particular for the smallest EVs, which can be as little as 40 nm in diameter, the limited number of antigens present, and their low refractive index. We addressed these limitations for detection and characterization of EV by flow cytometry through the use of multiplex and multicolor in situ proximity ligation assays (in situ PLA), allowing each detected EV to be easily recorded over background noise using a conventional flow cytometer. By targeting sets of proteins on the surface that are specific for distinct classes of EVs, the method allows for selective recognition of populations of EVs in samples containing more than one type of EVs. The method presented herein opens up for analyses of EVs using flow cytometry for their c...

The objective of this study was to determine if a relationship between microbial neoformation of volatiles and the post-mortem interval (PMI) exists, and if the volatiles could be used as a tool to improve the precision of PMI estimation in decomposed human remains found in an indoor setting. Chromatograms from alcohol analysis (femoral vein blood) of 412 cases were retrospectively assessed for the presence of ethanol, N-propanol, 1-butanol, and acetaldehyde. The most common finding was acetaldehyde (83% of the cases), followed by ethanol (37%), N-propanol (21%), and 1-butanol (4%). A direct link between the volatiles and the PMI or the degree of decomposition was not observed. However, the decomposition had progressed faster in cases with microbial neoformation than in cases without signs of neoformation. Microbial neoformation may therefore act as an indicator of the decomposition rate within the early decomposition to bloating stages. This may be used in PMI estimation based on t...