Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management.

Glioblastoma is a highly heterogeneous and infiltrative form of brain cancer associated with a poor outcome and limited therapeutic effectiveness. The extent of the surgery is related to survival. Reaching an accurate diagnosis and prognosis assessment by the time of the initial surgery is therefore paramount in the management of glioblastoma. To this end, we are studying the performance of SpiderMass, an ambient ionization mass spectrometry technology that can be used in vivo without invasiveness, coupled to our recently established artificial intelligence pipeline. We demonstrate that we can both stratify isocitrate dehydrogenase (IDH)-wild-type glioblastoma patients into molecular sub-groups and achieve an accurate diagnosis with over 90% accuracy after cross-validation. Interestingly, the developed method offers the same accuracy for prognosis. In addition, we are testing the potential of an immunoscoring strategy based on SpiderMass fingerprints, showing the association between prognosis and immune cell infiltration, to predict patient outcome.

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis.

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

Sub-pixel electron detection using a convolutional neural network.

Modern direct electron detectors (DEDs) provided a giant leap in the use of cryogenic electron microscopy (cryo-EM) to study the structures of macromolecules and complexes thereof. However, the currently available commercial DEDs, all based on the monolithic active pixel sensor, still require relative long exposure times and their best results have only been obtained at 300 keV. There is a need for pixelated electron counting detectors that can be operated at a broader range of energies, at higher throughput and higher dynamic range. Hybrid Pixel Detectors (HPDs) of the Medipix family were reported to be unsuitable for cryo-EM at energies above 80 keV as those electrons would affect too many pixels. Here we show that the Timepix3, part of the Medipix family, can be used for cryo-EM applications at higher energies. We tested Timepix3 detectors on a 200 keV FEI Tecnai Arctica microscope and a 300 keV FEI Tecnai G2 Polara microscope. A correction method was developed to correct for per-pixel differences in output. Timepix3 data were simulated for individual electron events using the package Geant4Medipix. Global statistical characteristics of the simulated detector response were in good agreement with experimental results. A convolutional neural network (CNN) was trained using the simulated data to predict the incident position of the electron within a pixel cluster. After training, the CNN predicted, on average, 0.50 pixel and 0.68 pixel from the incident electron position for 200 keV and 300 keV electrons respectively. The CNN improved the MTF of experimental data at half Nyquist from 0.39 to 0.70 at 200 keV, and from 0.06 to 0.65 at 300 keV respectively. We illustrate that the useful dose-lifetime of a protein can be measured within a 1 second exposure using Timepix3.