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Cell migration plays a crucial role in different pathological conditions, so a good understanding of the intra and extra cellular mechanisms involved could help in the development of new therapeutical strategies. Time-lapse microscopy is an excellent tool for dynamically studying cell movement. This work reports the development and testing of an integrated analysis environment based on MotoCell, a web application able to simultaneously manage time-lapse experiments, perform comparative analysis of multiple experiments extracting movement parameters, test models describing cell movement and proliferation and simulate the behaviour of cells growing on Petri dish via a simulation tool able to reproduce and predict the behaviour of cultured cells. The system includes additional tools that deal with acquisition and analysis of microscopy images and graphical representation of datasets containing cell paths obtained by tracking procedures.

Tracing the appearance and evolution of virus variants is essential in the management of the COVID-19 pandemic. Here, we focus on SARS-CoV-2 spread in Italian patients by using viral sequences deposited in public databases and a tracing procedure which is used to monitor the evolution of the pandemic and detect the spreading, within the infected population of emergent sub-clades with a potential positive selection. Analyses of a collection of monthly samples focused on Italy highlighted the appearance and evolution of all the main viral sub-trees emerging at the end of the first year of the pandemic. It also identified additional expanding subpopulations which spread during the second year (i.e., 2021). Three-dimensional (3D) modelling of the main amino acid changes in mutated viral proteins, including ORF1ab (nsp3, nsp4, 2’-o-ribose methyltransferase, nsp6, helicase, nsp12 [RdRp]), N, ORF3a, ORF8, and spike proteins, shows the potential of the analysed structural variations to result in epistatic modulation and positive/negative selection pressure. These analyzes will be of importance to the early identification of emerging clades, which can develop into new “variants of concern” (i.e., VOC). These analyses and settings will also help SARS-CoV-2 coronet genomic centers in other countries to trace emerging worldwide variants.

Although the simple diffusion model can effectively describe the movement of eukaryotic cells on a culture surface observed at relatively low sampling frequency, at higher sampling rates more complex models are often necessary to better fit the experimental data. Currently available models can describe motion paths by involving additional parameters, such as linearity or directional persistence in time. However sometimes difficulties arise as it is not easy to effectively evaluate persistence in presence of a directional bias. Here we present a procedure which helps solve this problem, based on a model which describes displacement as the vectorial sum of three components: diffusion, persistence and directional bias. The described model has been tested by analysing the migratory behaviour of simulated cell populations and used to analyse a collection of experimental datasets, obtained by observing cell cultures in time lapse microscopy. Overall, the method produces a good description of migration behaviour as it appears to capture the expected increase in the directional bias in presence of wound without a large concomitant increase in the persistence module, allowing it to remain as a physically meaningful quantity in the presence of a directional stimulus.

Molecular cytopathology is a rapidly evolving field embracing both conventional microscopy and molecular pathology. Its growing popularity stems from the fact that in many types of advanced cancers, including non small cell lung cancer (NSCLC), cytological samples often constitute the only available specimens for morphomolecular analysis. Indeed, non formalin fixed and paraffin embedded (FFPE) cytological samples feature a higher quality of extracted nucleic acids than histological specimens. However, because of the growing complexity of molecular testing, several efforts should be made to validate the analytical performance of the wide array of currently available molecular technologies, including next generation sequencing (NGS). This technology has the terrific advantage of allowing simultaneous detection of scores of predictive biomarkers even in low-input DNA/RNA specimens. Here, we briefly review the role of the modern cytopathologist in the morphomolecular diagnosing of advanced stage NSCLC and the adoption of NGS in conventional cytopreparations (cell blocks, direct smears, and liquid-based cytology) and supernatants.

Gene fusions represent novel predictive biomarkers for advanced non-small cell lung cancer (NSCLC). In this study, we validated a narrow NGS gene panel able to cover therapeutically-relevant gene fusions and splicing events in advanced-stage NSCLC patients. To this aim, we first assessed minimal complementary DNA (cDNA) input and the limit of detection (LoD) in different cell lines. Then, to evaluate the feasibility of applying our panel to routine clinical samples, we retrospectively selected archived lung adenocarcinoma histological and cytological (cell blocks) samples. Overall, our SiRe RNA fusion panel was able to detect all fusions and a splicing event harbored in a RNA pool diluted up to 2 ng/&micro, L. It also successfully analyzed 46 (95.8%) out of 48 samples. Among these, 43 (93.5%) out of 46 samples reproduced the same results as those obtained with conventional techniques. Intriguingly, the three discordant results were confirmed by a CE-IVD automated real-time polymerase chain reaction (RT-PCR) analysis (Easy PGX platform, Diatech Pharmacogenetics, Jesi, Italy). Based on these findings, we conclude that our new SiRe RNA fusion panel is a valid and robust tool for the detection of clinically relevant gene fusions and splicing events in advanced NSCLC.

Background Immune-checkpoint inhibitors (ICIs) represent an important treatment option for patients who have advanced stage non-small cell lung cancer (NSCLC). Currently, evaluation of the expression level of programmed death-ligand 1 (PD-L1) has proven highly successful as a positive predictive biomarker for ICIs. In addition to PD-L1, other promising predictive biomarkers are emerging, including high tumor mutational burden (TMB-H). However, measuring TMB-H remains challenging for several reasons, among which is the difficulty in obtaining adequate tissue material from NSCLC patients. There are no data in the current literature regarding the possibility of adopting cell blocks (CBs) for TMB evaluation; therefore, our goal was to evaluate the feasibility of analyzing TMB on CBs. Methods For evaluation of differences in run metric parameters, 8 pairs of histological and CB samples from patients with NSCLC were analyzed using the Oncomine Tumor Mutational Load Assay on Ion Torrent S5 GS next-generation sequencing (NGS) platform. Results Most CBs (6/8, 75.0%) were successfully analyzed by adopting the broad NGS panel approach. CBs provided results similar to those obtained on histological matched specimens in terms of median total reads (7207048.80 vs 7558817.80), median mapped reads (7075753.83 vs 7513822.00), median read lengths (115.50 vs. 113.00), median percentage of reads on-target (97.49% vs. 98.45%), median average reads per amplicon (454.67 vs 476.14), and median uniformity of amplicon coverage (83.52% vs 84.13%). Conclusion In this pilot study, we demonstrated the technical feasibility of assessing TMB on CBs.

The cancer secretome is a valuable reservoir of cancer biomarkers. Besides containing circulating tumor cells, extracellular vesicles, and proteins, it is also rich in circulating tumor DNA (ctDNA)-a subpopulation of cell free DNA. The most efficient technology to capture ctDNA is next generation sequencing (NGS). Indeed, this analysis enables the identification of both quantitative (e.g., mutant allelic fraction - MAF) and qualitative (e.g., the variant type) information. Strikingly, by calculating these data in relation to time, cytopathologists can decodify and graphically report the ctDNA "message", which may help to diagnose cancer, define treatment, and monitor disease evolution. In this paper, we report the most compelling evidence steadily accumulating on the successful application of NGS-based ctDNA analysis in cancer diagnosis, treatment decision, and monitoring of cancer progression. We also propose a mathematical model that calculates MAF evolution in relation to time.