Publications
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful label-free technique for mapping the spatial distribution of biomolecules directly from tissue. However, like most other MSI techniques, it suffers from low ionization yields and ion suppression effects for biomolecules that might be of interest for a specific application at hand. Recently, a form of laser postionization was introduced (coined MALDI-2) that critically boosts the ion yield for many glyco- and phospholipids by several orders of magnitude and makes the detection of further biomolecular species possible. While the MALDI-2 technique is being increasingly applied by the MSI community, it is still only implemented in fine vacuum ion sources in a pressure range of about 1–10 mbar. Here, we show the first implementation of the technique to a custom-built atmospheric pressure ion source coupled to an Orbitrap Elite system. We present results from parameter optimization of MALDI-2 at atmospheric pressure, compare our findings to previously published fine vacuum data, and show first imaging results from mouse cerebellum with a 20 μm pixel size. Our findings broaden the feasibility of the technique to overall more flexible atmospheric pressure ion sources.
Objective: The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our clinical task is to use mammographic features to predict whether ductal carcinoma in situ (DCIS) identified at needle core biopsy will be later upstaged or shown to contain invasive breast cancer.
Methods: To improve the prediction of pure DCIS (negative) versus upstaged DCIS (positive) cases, this study considers the adjunctive roles of two related classes: atypical ductal hyperplasia (ADH), a non-cancer type of breast abnormity, and invasive ductal carcinoma (IDC), with 113 computer vision based mammographic features extracted from each case. To improve the baseline Model A's classification of pure vs. upstaged DCIS, we designed three different strategies (Models B, C, D) with different ways of embedding features or inputs.
Results: Based on ROC analysis, the baseline Model A performed with AUC of 0.614 (95% CI, 0.496-0.733). All three new models performed better than the baseline, with domain adaptation (Model D) performing the best with an AUC of 0.697 (95% CI, 0.595-0.797).
Conclusion: We improved the prediction performance of DCIS upstaging by embedding two related pathology classes in different training phases.
Significance: The three new strategies of embedding related class data all outperformed the baseline model, thus demonstrating not only feature similarities among these different classes, but also the potential for improving classification by using other related classes.
Acrylamide is a suspected human carcinogen formed during high-temperature cooking of starch-rich foods. It is metabolised by cytochrome P450 2E1 to its reactive metabolite glycidamide, which forms pre-mutagenic DNA adducts. Using the human TP53 knock-in (Hupki) mouse embryo fibroblasts (HUFs) immortalisation assay (HIMA), acrylamide- and glycidamide-induced mutagenesis was studied in the tumour suppressor gene TP53. Selected immortalised HUF clones were also subjected to next-generation sequencing to determine mutations across the whole genome. The TP53-mutant frequency after glycidamide exposure (1.1 mM for 24 h, n = 198) was 9% compared with 0% in cultures treated with acrylamide [1.5 (n = 24) or 3 mM (n = 6) for 48 h] and untreated vehicle (water) controls (n = 36). Most glycidamide-induced mutations occurred at adenines with A > T/T > A and A > G/T > C mutations being the most common types. Mutations induced by glycidamide occurred at specific TP53 codons that have also been found to be mutated in human tumours (i.e., breast, ovary, colorectal, and lung) previously associated with acrylamide exposure. The spectrum of TP53 mutations was further reflected by the mutations detected by whole-genome sequencing (WGS) and a distinct WGS mutational signature was found in HUF clones treated with glycidamide that was again characterised by A > G/T > C and A > T/T > A mutations. The WGS mutational signature showed similarities with COSMIC mutational signatures SBS3 and 25 previously found in human tumours (e.g., breast and ovary), while the adenine component was similar to COSMIC SBS4 found mostly in smokers’ lung cancer. In contrast, in acrylamide-treated HUF clones, only culture-related background WGS mutational signatures were observed. In summary, the results of the present study suggest that glycidamide may be involved in the development of breast, ovarian, and lung cancer.
Background & Aims: Severe injury to the lining of the stomach leads to changes in the epithelium (reprogramming) that protect and promote repair of the tissue, including development of spasmolytic polypeptide-expressing metaplasia (SPEM) and tuft and foveolar cell hyperplasia. Acute gastric damage elicits a type-2 inflammatory response that includes production of type-2 cytokines and infiltration by eosinophils and alternatively activated macrophages. Stomachs of mice that lack interleukin 33 (IL33) or interleukin 13 (IL13) did not undergo epithelial reprogramming after drug-induced injury. We investigated the role of group 2 innate lymphoid cells (ILC2s) in gastric epithelial repair.
Methods: Acute gastric injury was induced in C57BL/6J mice (wild-type and RAG1 knockout) by administration of L635. We isolated ILC2s by flow cytometry from stomachs of mice that were and were not given L635 and performed single-cell RNA sequencing. ILC2s were depleted from wild-type and RAG1-knockout mice by administration of anti-CD90.2. We assessed gastric cell lineages, markers of metaplasia, inflammation, and proliferation. Gastric tissue microarrays from patients with gastric adenocarcinoma were analyzed by immunostaining.
Results: There was a significant increase in the number of GATA3-positive ILC2s in stomach tissues from wild-type mice after L635-induced damage, but not in stomach tissues from IL33-knockout mice. We characterized a marker signature of gastric mucosal ILC2s and identified a transcription profile of metaplasia-associated ILC2s, which included changes in expression of Il5, Il13, Csf2, Pd1, and Ramp3; these changes were validated by quantitative polymerase chain reaction and immunocytochemistry. Depletion of ILC2s from mice blocked development of metaplasia after L635-induced injury in wild-type and RAG1-knockout mice and prevented foveolar and tuft cell hyperplasia and infiltration or activation of macrophages after injury. Numbers of ILC2s were increased in stomach tissues from patients with SPEM compared with patients with normal corpus mucosa.
Conclusions: In analyses of stomach tissues from mice with gastric tissue damage and patients with SPEM, we found evidence of type 2 inflammation and increased numbers of ILC2s. Our results suggest that ILC2s coordinate the metaplastic response to severe gastric injury.
The biological importance and varied metabolic capabilities of specific microbial strains have long been established in the scientific community. Strains have, in the past, been largely defined and characterized based on microbial isolates. However, the emergence of new technologies and techniques has enabled assessments of their ecology and phenotypes within microbial communities and the human microbiome. While it is now more obvious how pathogenic strain variants are detrimental to human health, the consequences of subtle genetic variation in the microbiome have only recently been exposed. Here, we review the operational definitions of strains (e.g., genetic and structural variants) as they can now be identified from microbial communities using different high-throughput, often culture-independent techniques. We summarize the distribution and diversity of strains across the human body and their emerging links to health maintenance, disease risk and progression, and biochemical responses to perturbations, such as diet or drugs. We list methods for identifying, quantifying, and tracking strains, utilizing high-throughput sequencing along with other molecular and “culturomics” technologies. Finally, we discuss implications of population studies in bridging experimental gaps and leading to a better understanding of the health effects of strains in the human microbiome.