dataesr/scientific-paragraphs-categorization huggingface dataset:
D R A F T Here, we couple a unique in-vitro microfluidic technique to a multiscale in-silico RBC model that enables a quantitative approach of the mechanisms of passage of RBCs through interendothelial slits. The in-vitro technique allows the observation of the dynamics of passage of RBCs in slits of physiological dimensions, namely a submicron width, under tunable external stresses and slit sizes. The in-silico RBC model is implemented in a dynamic and a quasi-static versions. The dynamic version is integrated with a boundary integral simulation of surrounding flows to resolve the full fluid-cell interactions during this passage process, while the quasi-static version is done in commercial software ABAQUS  (23)  (see Materials and Methods and SI Appendix, Section 2). We showed that red blood cells are capable of amazing extreme deformations allowing them to pass through rigid slits as narrow as 0.
Conversely, the irreversible sickle RBC successfully passes through the slit (SI Appendix, Movie S6) as the cytoplasmic hemoglobin fibers probably aligns along the flow direction and its overall shape is sufficiently elongated to pass through the slit without further deformation. Obviously, however, the actual strain achieved by an RBC subjected to a given in-slit pressure drop, and in particular, its ability to form a thin tether within the slit, is limited by the cell rigidity. This actual strain determines the threshold dimension of the slit that the cell can pass through. Mechanical aspects: spectrin unfolding and membrane deformation. To have a deeper insight into the involved mechanical processes of deformation, we numerically calculated using ABAQUS software the minimum area, Sm, required for an RBC of given volume V to pass through the reference slit under an in-slit pressure drop of 500 Pa.
S11 ). Video analysis. Analysis of RBC retention was done using FIJI software (ImageJ): retained RBCs were counted with respect to the total of retained and passing RBCs. The retention rate was considered as significant for a percentage of retained RBCs higher than 10% in a population of at least 50 RBCs. Analysis of RBC transit through slits was carried out either manually (FIJI) or using a custom Matlab routine: the transit time was measured as the time spent by an RBC in contact with the slit. Analysis of RBC intracellular calcium was done using FIJI: the calcium fluorescence intensity was measured over the RBC projected area upstream of, within, and downstream of the slit. Dynamic and quasi-static simulations of RBCs passing through slits. For dynamic simulations, we applied an in-house code with coupled finite element (FEM) and boundary integral methods to study the dynamics of RBCs passing through the slits and their transit time with full fluid-structure interaction considered  (50).
We simulated the full fluid-cell interaction under prescribed pressure drop. The boundary integral method was used to simulate the surround Stokes flows, while the FEM was used to model the RBC lipid bilayer and cytoskeleton as two interacting shells. The same mesh of lipid bilayer was used in finite element and boundary integral simulations so that the velocity and stress jump conditions on the interface were satisfied. For quasi-static simulations, we applied the commercial FEM package ABAQUS  (23)  to study the retention of RBCs in the slits and the corresponding critical pressure drop without modeling the fluid flow explicitly, because the dynamics, such as viscosity, does not play a role in the critical pressure and the quasi-static approach is much more efficient.
Flow experiments and microscopy Microfluidic experiment. The device inlets and outlet were connected via Teflon tubing (0. 015" and 0. 027" inner and outer diameters, Scientific Commodities) to a flow controller system (MFCS-8C, Fluigent) that allowed controlling the pressure drop between inlets and outlet and the circulation of cell suspensions in the microfluidic device. The in-slit pressure drop ∆P was calculated afterwards using COMSOL® software, from the inlet/outlet pressure drop and the number of open/clogged slits during the experiment Fig. 11. Devices with various series of slits (fixed width W , length L, and depth D in each series) were placed on an inverted microscope (IX71, Olympus) equipped with cameras (high-speed camera Fastcam Mini, Photron and sCMOS camera Neo 5. 5, Andor), 20x/60x/100x objectives, and temperature control.
Video analysis Video analysis was mostly carried out manually using FIJI software. As a control before performing a complete analysis, the RBC projected radius was computed from an elliptic fit of the RBC projection. When RBCs reached the slits with abnormal shape (non discocyte) or radius (too large or too small), the data set was discarded. Retention analysis. An RBC is considered retained if it stays in a slit more than 2 sec. When a slit is blocked by an RBC, those which arrive afterwards on the same slit are not counted. The retention rate is the ratio of the number of retained RBCs to the total number of RBCs (transiting and retained) which is at least 50. Retention is considered significant when it is higher than 10%. Transit analysis. Using FIJI software the RBC position was tracked, and the RBC projected radius, shape and transit time were determined.
RBCs were classified from their shape in two categories: "dumbbell" shape with rounded projections on each side of the slit; "tip" shape with a thin protrusion at the RBC front exiting from the slit. The manual processing was double-checked using Matlab routines. To measure automatically the RBC transit time, we designed an automated Matlab procedure to segment and track RBCs on live imaging. We first spatially registered images, from time-point to time-point, to isolate moving RBCs from the static background by subtracting the median projection of the overall movie from the movie itself. We then segmented RBCs using thresholding and applied a series of binary operations to improve the resulting object masks (holes filling, opening, erosion and size filtering). To reduce tracking mistakes, we also discarded aggregated RBCs (two or more touching RBCs) using a supervised KNN clustering algorithm considering features such as cell area and circularity.
Overview of the modeling approaches While we applied an in-house code of coupled the finite element method (FEM) and boundary integral method to study the dynamics of RBCs passing through the slits with full fluid-structure interaction considered, we also applied the commercial FEM package ABAQUS  (8)  to study the retention of the RBCs passing through the slits as a quasi-static process without modeling the fluid flow explicitly. The reasons we used ABAQUS instead of the in-house code to study retention are: 1) Near the critical condition of retention, the process is so slow that it becomes too long for the in-house code to simulate, while it D R A F T approaches a quasi-state process so that ABAQUS can simulate the process much more efficiently and accurately; 2) For the retention study, we are only interested in the critical pressure for the cells to pass rather than its dynamics, so that it is not necessary to apply the in-house code for dynamics; 3) To accurately predict the critical pressure, the experimental measured area modulus of the lipid bilayer (375-450 mN/m) (7) must be used, because the area deformation plays a significant role on the critical pressure for retention which must be accurately captured even it is small.
Multiscale modeling of RBC membranes and spectrin unfolding. A multiscale RBC model was implemented in both the in-house code and an ABAQUS VUMAT user subroutine for modeling material properties  (8). In the whole-cell continuum-level model, the Cauchy stress resultant is given as  [2]  where τm and µ are the membrane mean stress resultant and shear modulus. B = F F T is the left Cauchy-Green deformation tensor and h is the thickness of the membrane modeled as a thin shell. F is the deformation gradient. I is the identity matrix, α = λ1λ2 -1 and β = (λ 2 1 + λ 2 2 )/(2λ1λ2) -1 are two invariants representing the area and shear deformation respectively  (7). λ1 and λ2 are the principal stretches, which can be calculated from F. Details of the formulation of the multiscale model, including the expressions of τm and µ, can be found in Ref.
ACKNOWLEDGMENTS. The project leading to this publication has received funding from  France 2030 , the  French Government  program managed by the  French National Research Agency  ( ANR-16-CONV-0001  and  ANR-20-CE17-0024 ). We thank  I. Ozerov  and  F. Bedu  from  PLANETE microfabrication facility. We thank  Pr. Pierre Buffet  and his group for providing us spherocytic and irreversible sickle RBCs. Z. P. and H. L. acknowledge the supports from  US National Science Foundation  grants  NSF   CBET-1706436/1948347  and  NSF   DMS-1951526. E. H. belongs to the  French Consortium AQV.
Frameworks and libraries To ease the development process, the implementation of the control system is based on frameworks and libraries. This facilitates debugging and application maintenance, and it presents a time-saving, a more efficient and cleaner way of implementing. When possible, the frameworks already in place in other ITER systems were re-used, and when not, custom solutions for the EC system were developed. The Fast Controller development is based on the MARTe  [6]  framework. MARTe is a C++ modular and multi-platform framework for the development of realtime control system applications. The main advantages of the framework are the separation between the platform specific implementation, the environment details and the user code, as well as the software quality assurance process of the components.
metabolomics and lipidomics data using the GBM dataset from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)  6. We developed a computational approach, Substrate PHosphosite-based Inference for Network of KinaseS (SPHINKS) to generate unbiased kinome-phosphosite networks and extract the master kinases (MKs) driving GBM subtypes. We experimentally validated protein kinase Cδ (PKCδ) and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) as the MKs that sustain cell growth and tumor cell identity of the glycolytic/plurimetabolic (GPM) and proliferative/   The number (n) of GBM samples for each subtype is indicated. For each subtype, representative genes with the highest frequency of fCNV prot gain (red squares) or loss (blue squares) are listed. wt, wild type; NES, normalized enrichment score; FDR, false discovery rate; GST, gene set test.
We performed a three-way comparison. The GPM subtype exhibited clear association with the mesenchymal subtypes of TCGA and MNP classifiers. Conversely, MTC tumors were mapped to all TCGA and MNP subtypes, with slight preference for RTK II and mesenchymal subtype in the TCGA and CPTAC dataset, respectively (Extended Data Fig. 1d -f and Supplementary Table  1a, b ). PPR and NEU had limited overlap with the TCGA and MNP classes, with proneural and RTK I contributing to most PPR and NEU tumors (Extended Data Fig. 1d ,e and Supplementary Table  1a, b ). Although the epigenetic RTK III, MID, MYCN and G34 subtypes were only minimally represented in TCGA and CPTAC datasets (4. 5% and 1. 2%, respectively), six of nine tumors were classified as PPR (Extended Data Fig. 1d, e ). We also compared functional subtypes with proneural-like, classical-like and mesenchymal-like subtypes reported by CPTAC  6.
Fig. 2  | Association between demographic, imaging-based features and functional subtypes. a, Forest plots of age and sex association with GBM functional subtypes or the aggregated of PPR and NEU in the TCGA dataset (n = 503 GBM samples; univariate logistic regression). log(OR) estimates, 95% confidence intervals (CI) and P values are reported (*: P < 0. 10; **: P < 0. 05). OR, odds ratio. log(OR) estimates higher/lower than 0 represent positive/negative association. b, Forest plots of the association between tumor location and GBM functional subtypes in the TCGA dataset (n = 88 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and P values are reported. c, Bar plots showing the proportion of necrosis and edema in functional subtypes of GBM from the TCGA cohort (n = 63 GBM samples) and deep white matter (WM) invasion from TCGA (n = 40 GBM samples) and REMBRANDT (n = 14 GBM samples) datasets.
d, Forest plots of the association between contrast-enhancing, non-contrast-enhancing tumor or edema and GBM functional subtypes in the TCGA dataset (n = 88 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and P values are reported. e, Forest plot of the association between contrast-enhancing or non-contrast-enhancing tumor and metabolic or neurodevelopmental GBM subtypes in the TCGA dataset (n = 88 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and P values are reported. f, Unsupervised clustering on 175 differential quantitative radiomic features in GBM subtypes (n = 88 GBM samples, left; two-sided MWW test). Top track shows clusters; bottom track shows tumor classification. Representative radiomic features for cluster 1 (enriched with PPR tumors) and cluster 4 (enriched with GPM tumors) are indicated.
Sustained RS and DDR signaling characterizes PPR GBM The proteomic profiling of PPR GBM combined molecular marks of proliferation with activation of DDR (Fig. 1b ). Moreover, PPR tumors exhibited overrepresentation of DNA replication/replication fork and DNA double-strand break repair (DDSB) proteins, suggesting that enhanced RS may promote DDR signaling (Fig. 4a ). To test this hypothesis, we performed data mining and ontology integration from mass-spectrometry datasets to identify phosphosites increased in cells treated with irradiation, which causes DDSB lesions, ATR inhibitors or hydroxyurea that induce RS (Methods). We selected 15 and 16 experimentally validated phosphosites specific for cells undergoing DDSB and RS, respectively and 3 phosphosites common to DDSB and RS. Compared to other tumor subtypes, PPR contained elevated levels S Q S T M 1 S 2 8 Y A P 1 S 6 1 S Q S T M 1 T 2 6 9 S 2 7 2 IK Z F 1 S 6 3 M A P 3 K 3 S 1 9 7 A R H G A P 1 5 S 4 3 H M G N 1 S 7 T M E M 2 2 9 A S 4 H M G N 1 S 7 S E T X S 2 6 6 8 N E LF A T2 8 8 YA P1 S6 1 VI M S2 9 GJ A1 T3 26 S3 28 S3 30 VIM S2 9 VAS P S32 2 HMG N1 S7 PHACTR 2 S571 VIM S29 BAD S118 VIM S419 HM GN 1 S7 KIA A12 17 T6 8 VI M S2 9 H M G N 1 S7 S ET X S 26 6 8 V IM S 2 5 Y A P 1 S 6 1 H M G N 1 S 7 K IA A 1 2 1 7 T 6 8 A R H G E F 2 S 1 5 1 F O X O 3 S 2 5 3 P T P N 1 2 S 4 3 5 B A D S 1 1 8 V IM S 2 9 H M G N 1 S 7 K IA A 1 H1 FX S3 1 KIF 4A S8 01 LMN B1 S23 TOP2 A S1106 TMF1 S199 PKC D V R K 2 P 3 8 D S Y K M K -2 MN K1 AM PK A1 IK K B S 6 K 2 P H K G 2 G S K 3 B PK CE PAK 3 P A K 1 J N K 3 T T B K 2 B R A F CH K2 CDK 2 D N A P K C D K 6 C K 2 A 1 C D K 1 RA F1 GBM GPM MTC NEU PPR.
Master kinase analysis uncovers GBM subtype-specific kinases and actionable dependencies To begin exploring the phospho-proteomics landscape of GBM subtypes and their organization, we cataloged phosphosites specific for each GBM subtype and applied the outlier enrichment analysis. We obtained four phosphosite modules of overrepresented pathways that summarized previously assigned subtype hallmarks (Fig. 5a  and Supplementary Table  5a-c ). We then sought to link phosphosite enrichment to the activity of GBM subtype-specific protein kinases. To this aim, we developed SPHINKS, which integrates proteomics and phospho-proteomics profiles to build an interactome of kinasephospho-substrate pairs that are scored according to the strength of their interaction across all samples (Fig. 5b ). The GBM-specific kinase-phosphosite interaction network was generated using a semi-supervised support vector machine (SVM) algorithm trained on experimentally validated kinase-substrate phosphosite pairs from the PhosphoSitePlus database  30.
We used a dataset reporting changes in the abundance of phospho-proteins after perturbation of upstream kinases  33, 34  (103 kinase perturbation for 30 kinases and 61,181 phosphosites, the 'gold standard') and the metric defined as 'top-k-hit', which focuses on the top kinase predictions  34. SPHINKS produced higher activity scores than other methods and was superior in correctly identifying the perturbed Experiments were performed twice with similar results. b, Viability curves of GPM PDOs (n = 14 PDOs, each derived from an independent patient) treated with BJE6-106. Data in each curve are mean ± s. d. of n = 6 or 18 technical replicates for each PDO (Source Data Fig. 6 ). The experiment was repeated three times with similar results. c, Colony-forming assay using GPM PDO cells treated with BJE6-106.
Functionally conserved pediatric and adult cancer subtypes share MKs In an effort to ascertain whether the key biological functions discriminating the GBM subtypes coalesce into grouping patterns sharing the same kinase-driven dependencies, we first determined whether a functional classification could be obtained in PG, BRCA and LSCC for which genomics, proteomics and phospho-proteomics datasets are available  [46] [47] [48]. For PG, we integrated protein and gene expression data of 103 samples classified as high-grade (PG-HGG) or low-grade (PG-LGG) gliomas using SNF (Supplementary Tables  1d  and 6a ). We identified four subtypes of PG, recapitulating the functional classifier of GBM for proteomic, phospho-proteomic and gene expression data (GPM, MTC, PPR and NEU; Fig. 7a  and Supplementary Table 6b-g).
8a ,b and Supplementary Tables 1f,g and 7h-k). The positive association between PPR and Basal-I subtype was further supported by the strong enrichment of DNA replication and proliferation-associated pathways in the Basal-I subtype (Fig. 7d ). Consistent with the prolonged survival of LumA-I, the MTC-BRCA subtype had a significantly better prognosis (Extended Data Fig. 8c ). Finally, we used the functional classifier to segregate 106 LSCC tumors and tested the association with the five known LSCC-specific molecular NMF-based subtypes described by CPTAC (Fig. 7f ,g and Supplementary Tables  1h  and 7l -r). LSCC tumors were classified into two major subtypes (GPM and PPR) and a much smaller MTC subgroup. In this limited dataset we did not identify NEU tumors. We found a positive correlation of the MTC subtype with the Basal-I subgroup.
EMT and inflamed secretory LSCC subtypes as two independent groups were functionally unified by the activation of immune, epithelial-to-mesenchymal transition (EMT) and angiogenesis functions of the GPM subtype. The PPR subtype included proliferative-primitive and classical subtypes, both sustained by proliferative-related pathways (Fig. 7f, g )  48, 51. The robustness of the functional subtyping was validated in the TCGA-LUSC (lung squamous carcinoma) dataset (Extended Data Fig. 8d  and Supplementary Tables  1i  and 7s ,t). In this larger cohort, 12 tumors exhibited activation of synaptic functions, a hallmark of the NEU subtype. MTC-LUSC tumors exhibited more favorable clinical outcomes, suggesting that also in this tumor type OXPHOS activation produces a less aggressive biology and/ or increases sensitivity to therapy (Extended Data Fig.
8e )  5. Dependency of BRCA and LUSC MTC cells on mitochondrial activity was supported by the association between MTC activity of BRCA and LUSC cell lines in the DepMap dataset  52  and sensitivity to menadione, a cytotoxin that specifically targets mitochondria (Extended Data Fig. 8f ). Next, we applied SPHINKS to generate tumor-specific kinasephosphosite interactomes for PG, BRCA and LSCC, including 669, 1,399 and 1,985 kinase-phosphosite relationships from 76, 198 and 103 kinases and 210, 1,899 and 699 phosphosites for PG, BRCA and LSCC, respectively and identified subtype-specific MKs (Supplementary Tables 8-10 and Extended Data Fig. 9 ) that we validated by global protein abundance and mRNA expression (Supplementary Tables  8 9 10 ). Most subtype-specific MKs were activated only in one tumor type (Extended Data Fig.
Development of a probabilistic functional classifier of GBM We designed an algorithm for the probabilistic classification of individual tumors into GBM functional subtypes. When compared to RNA derived from fresh frozen samples, FFPE-extracted RNA is characterized by lower quality, typically affecting different mRNA species to variable extent  57. Thus, we tested two classifiers, one informed by RNA-seq data from frozen tumor samples ('frozen model') and the other by RNA-seq data from FFPE tumors ('FFPE model'). For the frozen model, we trained the classifier using the multinomial regression model with lasso penalty on the TCGA IDH wild-type GBM dataset profiled by Agilent expression array, which we had classified in previous work (Extended Data Fig. 10a  and Supplementary Table  11a )  5.
The diagnostic ability of the classifier was confirmed by the AUROC of each subtype above 0. 85 in each validation dataset (Fig. 8a ). We determined the accuracy of the assignment of each tumor to the correct subtype  58. Misclassification error was < 18%, sensitivity approached 85%, specificity was close to 100% and precision > 80%, indicating a robust performance of the classifier (Fig. 8b  and Supplementary Table  11c ). The frozen model was validated on an independent cohort of 45 frozen samples for which matched FFPE samples were available (see below), obtaining similar results (Extended Data Fig. 10b ). For the FFPE model, to account for the lower quality of FFPE-extracted RNA, we sequenced the transcriptome of 45 frozen and FFPE matched samples and selected 4,668 genes that exhibited consistent expression profiles in both sample types (genes supposedly unaffected by FFPE treatment, Spearman correlation, ρ > 22; Supplementary Table  12 ).
With the classification of frozen samples as the gold standard, we generated subtype-specific signatures using expression profiles of the corresponding FFPE samples. We then trained the multinomial regression model using FFPE-specific signature genes from TCGA-GBM Agilent cohort (66 gene features, 19. 76% cross-validation error and 11. 07% error on the training set). The performance of the classifier purple, precision) in each GBM subgroup. The number (n) of GBM samples for each dataset is indicated. e, Functional activities, genetic alterations, MKs, clinical characteristics, radiomic features and therapeutic vulnerability compose modules that distinguish each functional subtype. GBM driver genes in each module recapitulate the functional hallmark of each subtype (for example, CDK6 amplification/CDKN2A deletion for the PPR proliferation/stemness features; MET amplification/NF1 deletion for glycolysis/RAS pathway activation in GPM GBM; FGFR3-TACC3 fusion for mitochondrial activation in MTC tumors).
Article https://doi. org/10. 1038/s43018-022-00510-x was assessed on an independent cohort of 133 FFPE samples profiled by RNA-seq, classifying 73% of the samples. To assess the stability and accuracy of the FFPE model, we unbiasedly assigned FFPE samples to a subtype by unsupervised consensus clustering of 178 samples (133 FFPE plus 45 FFPE with matched frozen specimens; Extended Data Fig. 10c ). Using the classification of the 45 frozen samples as 'anchors', we assigned each cluster to a functional GBM subtype and compared the resulting unbiased label assignment with the subtype classification from the FFPE model for the 133 unmatched FFPE samples only. The classifier performance indexes were similar to those calculated for the frozen model (misclassification error of 15%; AUROCs, sensitivity, specificity and precision > 0.