Biology subjects

Alguel, H.

Publications and source records attributed to Alguel, H..

3 recordsLinked to original sources

bioRxiv2025

DISCO-seq: 3D single-cell transcriptomics of intact biological systems

Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridgingmesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/693352v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@758ee5org.highwire.dtl.DTLVardef@1f862c6org.highwire.dtl.DTLVardef@1cf3b9org.highwire.dtl.DTLVardef@c5082e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-seq integrates RNA-preserving tissue-clearing chemistry with whole-organ or organism 3D imaging, enabling anatomically unbiased single-cell transcriptomics. C_LIO_LIDISCO-seq yields RNA quality and transcriptome profiles equivalent to those obtained from matched fresh or fixed tissues. C_LIO_LIDISCO-seq identifies discrete glioblastoma microenvironments and defines their transcriptomic states within the intact brain. C_LIO_LIDISCO-seq enables whole-body mapping of SARS-CoV-2 S1 and uncovers region-specific immune and metabolic responses across anatomical niches. C_LI Supplementary movies can be seen at: http://discotechnologies.org/DISCO-seq/

systems biology↗

bioRxiv2024

KRas, in addition to Tp53 is a driver for early carcinogenesis and a molecular target in a mouse model of invasive gastro-esophageal adenocarcinoma

ObjectiveThe incidence of gastro-esophageal adenocarcinoma (GEAC) has increased dramatically and is associated with Barretts Esophagus (BE). Gastric cardia progenitors are the likely origin for BE and GEAC. Here we analyze p53, Rb1 and Kras alterations in Lgr5 progenitor cells during carcinogenesis. DesignWe introduced single and combined genetic alterations (p53, Rb1 and Kras) in Lgr5-expressing progenitor cells at the inflamed gastroesophageal junction in the L2-IL1b (L2) mouse model crossed to Lgr5-CreERTmice. For in-vitro treatment we utilized mouse and human 3D organoids. ResultsInactivation of Tp53 or Rb1 alone (L2-LP and L2-LR mice) resulted in metaplasia, and mild dysplasia, while expression of KrasG12D (L2-LK) accelerated dysplasia in L2-IL1b mice. Dual induction of genetic alteration in L2-LPR, L2-LKP and L2-LKR mice confirmed the accelerating role of mutant Kras, with the development of invasive cancer in mice with combined Tp53 and Kras alteration. All three genetic events in cardia progenitor cells generated invasive cancer at 6 months of age, with chromosomal instability (CNV). The dominant role of Kras prompted us to treat with a SHP2 inhibitor in combination with an ERK or MEK inhibitor, leading to reduced growth in Kras mutant organoids. SHP2 and MEK inhibition in-vivo reduced Kras dependent tumor formation. ConclusionIn the first invasive GEAC mouse model, Kras mutation in combination with loss of tumor suppressor genes Tp53 or Rb1 emerges as a key player in GEAC and with importance of p53 and Rb1 in promoting metaplasia. Targeting this SHP2/MEK/KRAS pathway represents a promising therapeutic option for Kras altered GEAC. What is already known on this topicThe increased incidence of GEAC is challenging current screening and surveillance strategies. Therapeutic and preventive options are limited due to a lack of knowledge on the role of genetic alterations commonly associated with GEAC and their function during progression to dysplasia. What this study addsWe generate the first invasive GEAC model and show that KRAS at least in combination with a second genetic alterations (Tp53 and/or Rb1) may be a driver of tumorigenesis, and targeting KRAS alterations could be a promising now treatment substitution. How this study might affect research, practice or policyTargeting KRAS alterations will be important for GEAC, especially as specific KRAS inhibitor are on the horizon. In addition, a concept of single genetic alteration inducing metaplasia as an adaptation to chronic inflammation might emerge as an important factor for surveillance.

cancer biology↗

bioRxiv2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗