Biology subjects

Tissier, R.

Publications and source records attributed to Tissier, R..

4 recordsLinked to original sources

bioRxiv2022

How does target lesion selection affect RECIST? A computer simulation study

RECIST is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection. Disagreement among readers, and between readers and total tumor burden was analyzed, as a function of the total number of lesions, affected organs, and lesion growth. Disagreement aggravates when the number of lesions increases, when lesions are concentrated on few organs, and when lesion growth borders the thresholds of progressive disease and partial response. An intrinsic methodological error is observed in the estimation of total tumor burden (TTB) via RECIST. In a metastatic setting, RECIST displays a non-linear, unpredictable behavior. Our results demonstrate that RECIST can deliver an accurate estimate of total tumor burden in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions", which introduce a bias that makes it hardly possible to perform an accurate estimate of the total tumor burden. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.

cancer biology↗

bioRxiv2022

ShrinkCRISPR : A flexible method for differential fitness analysis of CRISPR-Cas9 screen data.

CRISPR screens provide large-scale assessment of cellular gene functions. Pooled libraries typically consist of several single guide RNAs (sgRNAs) per gene, for a large number of genes, which are transduced in such a way that every cell receives at most one sgRNA, resulting in the disruption of a single gene in that cell. This approach is often used to investigate effects on cellular fitness, by measuring sgRNA abundance at different time points. Comparing gene knockout effects between different cell populations is challenging due to variable cell-type specific parameters and between replicates variation. Failure to take those into account can lead to inflated or false discoveries. We propose a new, flexible approach called ShrinkCRISPR that can take into account multiple sources of variation. Impact on cellular fitness between conditions is inferred by using a mixed-effects model, which allows to test for gene-knockout effects while taking into account sgRNA-specific variation. Estimates are obtained using an empirical Bayesian approach. ShrinkCRISPR can be applied to a variety of experimental designs, including multiple factors. In simulation studies, we compared ShrinkCRISPR results with those of drugZ and MAGeCK, common methods used to detect differential effect on cell fitness. ShrinkCRISPR yielded as many true discoveries as drugZ using a paired screen design, and outperformed both drugZ and MAGeCK for an independent screen design. Although conservative, ShrinkCRISPR was the only approach that kept false discoveries under control at the desired level, for both designs. Using data from several publicly available screens, we showed that ShrinkCRISPR can take data for several time points into account simultaneously, helping to detect early and late differential effects. ShrinkCRISPR is a robust and flexible approach, able to incorporate different sources of variations and to test for differential effect on cell fitness at the gene level. These improve power to find effects on cell fitness, while keeping multiple testing under the correct control level and helping to improve reproducibility. ShrinkCrispr can be applied to different study designs and incorporate multiple time points, making it a complete and reliable tool to analyze CRISPR screen data.

bioinformatics↗

bioRxiv2021

{-}CardiOvascular examination in awake Orangutans (Pongo pygmaeus pygmaeus): Low-stress Echocardiography including Speckle Tracking imaging (the COOLEST method)

IntroductionCardiovascular diseases have been identified as a major cause of mortality and morbidity in Borneo orangutans (Pongo pygmaeus pygmaeus). Transthoracic echocardiography is usually performed under anesthesia in great apes, which may be stressful and risky in cardiac animals. The aim of the present pilot study was hence to develop a quick and non-stressful echocardiographic method (i.e., the COOLEST method) in awake Borneo orangutans (CardiOvascular examination in awake Orangutans: Low-stress Echocardiography including Speckle Tracking imaging) and assess the variability of corresponding variables. Materials and MethodsFour adult Borneo orangutans trained to present their chest to the trainers were involved. A total of 96 TTE examinations were performed on 4 different days by a trained observer examining each orangutans 6 times per day. Each examination included four two-dimensional views, with offline assessment of 28 variables (i.e., two-dimensional (n=12), M-mode and anatomic M-mode (n=6), Doppler (n=7), and speckle tracking imaging (n=3)), representing a total of 2,688 measurements. A general linear model was used to determine the within-day and between-day coefficients of variation. ResultsMean{+/-}SD (minimum-maximum) images acquisition duration was 3.8{+/-}1.6 minutes (1.3-6.3). All within-day and between-day coefficients of variation but one (n=55/56, 98%) were <15%, and most (51/56, 91%) were <10% including those of speckle tracking systolic strain variables (2.7% to 5.4%). DiscussionHeart morphology as well as global and regional myocardial function can be assessed in awake orangutans with good to excellent repeatability and reproducibility. ConclusionsThis non-stressful method may be used for longitudinal cardiac follow-up in awake orangutans.

zoology↗

bioRxiv2020

'Range': A gain and dynamic range independent quantification of spread aiding in panel design

In conventional flowcytometry one detector (primary) is dedicated for one fluorochrome. However, photons usually end up in other detectors too (fluorescence spillover). Compensation is a process that corrects the spillover signal from all detectors except the primary detector. Post compensation, the photon counting error of spillover signals become evident as spreading of the data. The spreading induced by spillover impairs the ability to resolve stained cell population from the unstained one, potentially reducing or completely losing cell populations. For successful multi-color panel design, it is important to know the expected spillover to maximize the data resolution. The Spillover Spreading Matrix (SSM) can be used to estimate the spread, but the outcome is dependent on detector sensitivity. Simply, the same single stained sample produces different spillover spread values when detector(s) sensitivity is altered. Many researchers mistakenly use this artifact to "reduce" the spread by decreasing detector sensitivity. This can result in diminished capacity to resolve dimly expressing cell populations. Here, we introduce SQI (Spread Quantification Index), that can quantify the spillover spread independent of detector sensitivity and independent of dynamic range. This allows users to compare spillover spread between instruments having different types of detectors, which is not possible using SSM.

immunology↗