Spotlight on 10x Visium: a multi-sample protocol comparison of spatial technologies

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 14, 2024

Background Spatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium platform, a popular method which enables transcriptomewide profiling sections. offers range sample handling and library construction methods introduces need for benchmarking compare data quality assess how well technology can recover expected features biological signatures. Results Here we present SpatialBench , unique reference dataset generated from spleen mice responding malaria infection spanning several preparation protocols (both fresh frozen FFPE samples, with without CytAssist placement). We noted better control metrics in samples prepared using probe-based methods, particularly those processed CytAssist, validating improvement produced platform. Our analysis replicate extends explore spatially variable detection, outcomes clustering cell deconvolution matched single-cell RNA-sequencing publicly identify types regions spleen. Multi-sample differential recovered known signatures related sex or knockout. Conclusions framed comprehensive multi-sample workflow that allowed us generate consistent results both between different subsets enabling broader comparisons interpretations made at group-level. dataset, analysis, serve as practical guide users may prove valuable other studies.

Language: Английский

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Nicheformer: a foundation model for single-cell and spatial omics

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 17, 2024

Tissue makeup relies fundamentally on the cellular microenvironment. Spatial single-cell genomics allows probing underlying interactions in an unbiased, scalable fashion. To learn a unified cell representation that accounts for local dependencies microenvironment, we propose Nicheformer, transformer-based foundation model combines human and mouse dissociated targeted spatial transcriptomics data. Pretrained over 57 million 53 spatially resolved cells across 73 tissues reconstruction, is fine-tuned tasks omics data to decode information. Nicheformer excels linear-probing fine-tuning scenarios novel set of downstream tasks, particular composition prediction label prediction. We further show existing models trained alone are not capable recapitulating complexity their microenvironments, indicating multiscale required understand complex at scale. enables context cells, allowing transfer rich information scRNA-seq datasets. Overall, sets stage next generation machine-learning analysis.

Language: Английский

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Comparative analysis of multiplexed in situ gene expression profiling technologies

Published: June 7, 2024

The burgeoning interest in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six methods, including both commercially available academically developed using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number unique molecules detected per cell, are not directly comparable across datasets due to substantial differences incidence off-target molecular artifacts impacting specificity. To address these challenges, explored various potential sources artifacts, novel metrics control them, utilized evaluate compare different technologies. Finally, demonstrate how false positives can seriously confound spatially-aware differential analysis, requiring caution interpretation downstream results. Our provides guidance selection, processing, spatial

Language: Английский

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Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

Genome biology, Journal Year: 2024, Volume and Issue: 25(1)

Published: June 12, 2024

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations fixed tissues. Normalization gene expression data is often needed to account for technical factors that may confound underlying biological signals.

Language: Английский

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Probe set selection for targeted spatial transcriptomics

Nature Methods, Journal Year: 2024, Volume and Issue: 21(12), P. 2260 - 2270

Published: Nov. 18, 2024

Abstract Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell subcellular resolution by measuring expression a predefined set genes. The selection an optimal probed genes is crucial for capturing signals present tissue. This requires selecting most informative, yet minimal, to profile (gene selection) which it possible build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous or new states. We Spapros, end-to-end probe pipeline that optimizes both gene specificity type identification within-cell variation resolve spatially distinct populations while considering prior knowledge as well design constraints. evaluated Spapros show outperforms other approaches recovery recovering beyond types. Furthermore, we used situ hybridization (SCRINSHOT) experiment adult lung tissue demonstrate how selected with identify interest detect even within

Language: Английский

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A practical guide to spatial transcriptomics

Molecular Aspects of Medicine, Journal Year: 2024, Volume and Issue: 97, P. 101276 - 101276

Published: May 21, 2024

Language: Английский

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Lessons learned from spatial transcriptomic analyses in clear-cell renal cell carcinoma

Nature Reviews Urology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Language: Английский

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Towards deciphering the bone marrow microenvironment with spatial multi-omics

Seminars in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 167, P. 10 - 21

Published: Jan. 30, 2025

Language: Английский

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Spatial-Omics Methods and Applications

Methods in molecular biology, Journal Year: 2025, Volume and Issue: unknown, P. 101 - 146

Published: Jan. 1, 2025

Language: Английский

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The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south

Frontiers in Digital Health, Journal Year: 2025, Volume and Issue: 7

Published: Feb. 6, 2025

Tumor boards are multidisciplinary teams of healthcare professionals that working together to encompass the full spectrum care around diagnosing, planning treatment, and advising outcomes for individual cancer patients. These typically consist oncologists, radiologists, pathologists, geneticists, surgeons, nurse practitioners, other palliative (National Cancer Institute, 2024). create a collaborative space experts from various disciplines assess clinical factors patient circumstances, ensuring application appropriate standards personalized recommendations National Comprehensive Network (NCCN) Guidelines enhance treatment met. Since no fits "textbook" profile, oncologists benefit discussing tailored plans learning their colleagues' experiences. When tumor functioning well, they can have significant impact on (NCCN, 2025). For instance, thoracic oncology board in Munich, Germany, found 90% met or exceeded standards, with nearly being implemented practice (Walter et al, 2023).Tumor increasingly used worldwide, but expertise resources conducting still limited Global South. However, this does not mean cannot be developing countries. A 2020 survey Southeast Asia 80.4% pediatric solid units had pediatric-trained specialists, including radiation nuclear medicine physicians, nurses. This indicates already place these specialists play critical role (Ottman, 2020). With implementation global south, data scientists further AI analytics improve decision-making personalize care.Advances big data, machine (ML), artificial intelligence (AI) provide more precise, evidence-based, patient-specific care, thus, giving different approach as how diagnose, treat, manage patients (Alowais 2023). there is growing number complexity industry such Electronic Health Records (EHRs), next-generation genomic sequencing (NGS), advanced imaging modalities like X-ray Radiography, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans. analyzing individually manually, time-consuming considerably impractical. where decision support systems (CDSS) powered by ML put into action. predictive analysis disease progression prognosis, based patients' drug-drug interaction alerts (Wang 2023;Alowais As precision continue evolve, will rely data-driven tools reduce errors, overall health (Khalifa Albadawy, Data process analyze large datasets identify biomarkers predict respond specific treatments (Nardone In addition, algorithms interpret radiological images, detect early signs cancer, progression. becoming standard boards, especially high-income countries (Bi 2019;El Saghir 2015).For oncology, most commonly diagnostic guide targeted therapies Polymerase Chain Reaction (PCR), fluorescent situ hybridization (FISH), immunohistochemistry (IHC) (Goosens 2015). high-throughput (NGS)-based diagnostics, which somatic mutations tumors, proven clinically useful identifying single-nucleotide mutations, insertions, deletions, rearrangements (Kamps 2017). Thus, multigene NGS testing oncologist picture molecular profile utilized best option (Mehta 2020).As continues gain prominence characterization cancers becomes complex (Specchia al., 2020;Nardone 2024), incorporating essential. bring ML, analysis, bioinformatics, enabling make accurate, evidence-based decisions lead improved 2024;Rodriguez Ruiz 2022). They synthesizing diverse generated uncovering actionable insights, informing strategies. particularly crucial shifts focus toward approaches genetic characteristics tumors (Subrahmanya, 2022).Specifically, apply statistical techniques survival clustering, modeling uncover insights inform decisions.Their knowledge foundation models, Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations Transformers (BERT), memory-augmented neural networks enables them extract valuable unstructured medical records pathology reports 2024;Wang 2023).Globally, trend towards integrating care. countries, AI-based assist clinicians interpreting predicting outcomes, optimal United States, example, institutions Memorial Sloan Kettering Center using tools, algorithms, real time during discussions. integration varies across regions, some low-and middle-income facing barriers adoption due lack infrastructure (Zuhair 2024).In States Europe, key members boards. at University Florida collaborate develop models (UF Health, AI-driven responses potential trials. Similarly, work real-world integrate it decision-making, each receives unique (Harris 2023).Data-driven provided revolutionized treatment. By large-scale datasets, driving suggest likely effective than (Berger Mardis, 2018). Predictive also forecast allowing tailor predicted response. trial-and-error nature efficient 2023).Al promising innovation imaging, applications ranging image acquisition processing reporting, follow-up planning, management. Given broad scope applications, anticipated daily radiologists (Pesapane The challenge, however, lies AI-powered equipment information training many professionals, radiologists. preparation may contribute reluctance adopt radiology fields (Waymel, 2019;Pesapane Nevertheless, transformative advancements only occurring academic hospitals highly facilities, regions communities grappling greatest challenges disparities (Sitek, 2024).While made strides science LMICs face challenges. include computational infrastructure, insufficient access high-quality shortage trained capable (Alami Additionally, concerns about algorithmic bias ethical implications healthcare, populations (Siala Wang, Overcoming require investment both technology human capital, well development frameworks use settings.In South, includes Asia, Africa, Latin America Caribbean (United Nations Development Programme (UNDP), 2004), often hampered outdated infrastructure. higher rates late-stage diagnoses poorer compared (Bamodu Chung, parts Sub-Saharan Africa travel long distances leading delays diagnosis (Mwamba Moreover, underfunded latest advances Just Philippines, main challenge difficulty financial toxicity brings family (Fernandez & Ting, Thus despite national incorporate country (Loong 2023) very challenging day-to-day practice. If cost were limiting factor, Philippines would managing (Catedral 2020).Data address South leveraging optimize resource allocation accuracy. telemedicine platforms mobile (mHealth) real-time rural areas (Haleem 2021;Akingbola progression, high risk complications, prioritize those need (Alowais, IBM's Watson Oncology (WFO), an CDSS therapy selection (Liu 2018), beneficial tool Hence, track monitor responses, 2019). applied even absence equipment, help regions.While studies settings LMICs, specifically limited. Kenya, screen cervical areas, significantly reducing While Ethiopia, been blood smear images diagnose leukemia accuracy (Akingbola examples demonstrate revolutionize providing affordable, scalable solutions pressing Academy International (USAID) has making efforts gap highlighting actions effectively promote (USAID, 2022).Transdisciplinarity emerged multiple integrated tackle problems angles. incorporates domains medicine, science, social sciences, ethics. pooling fields, providers offer comprehensive patients, (Van Bewer, Complex Hospital S.G. Bosco Turin, nurses psychologists, workers worked unmet needs innovative projects (Clementi Transdisciplinary successful strategy expediting emergency department (ED) flow. Through collaboration allied team was able efficiently, prompt delivery (Innes 2016). secondary BRIGHT Study chronic illness management after heart transplant revealed centers dedicated achieved better (p=0.042) (Cajita, Similar disciplines, leverage resources, project linking 54 million electronic England (Wood, 2021). highlight within transdisciplinary sectors.To must meet range technical, domain-specific, interpersonal requirements. modeling, essential, oncology-related omic records. Candidates should hold graduate-level degree discipline strong emphasis statistics mathematics, statistics, physics, biology, computer electrical engineering, biomedical engineering (BME), related fields. level ensures ability handle heterogeneous while adhering regulations similar Insurance Portability Accountability Act (HIPAA) maintain privacy confidentiality.A robust understanding terminology workflows seamless communication professionals. Furthermore, excel translating findings employing visualization facilitate disciplines. Beyond technical skills, abilities vital environment To ensure quality consistency contributions, eligibility regulation international professional bodies. Lastly, commitment continuous adapt emerging innovations medicine.Insights study Fermin Tan (2021), BME formal discipline, importance formalized educational pathways recognition healthcare. research demonstrated recognizing field achieve impactful innovations. Applying lessons emphasizes structured education programs regulatory LMIC contexts.Efforts standardize qualifications competencies EDISON Science Framework (EDSF), provides professionalization comprising components Competence (CF-DS), Body Knowledge (DS-BoK), Model Curriculum (MC-DS), Professional Profiles (DSPP) (Demchenko 2017a(Demchenko , 2017b(Demchenko 2017c(Demchenko 2017d)). American Medical Informatics Association (AMIA) competency-based accreditation informatics, aligns closely roles (Valenta Computing Machinery (ACM) supports computing undergraduate curricula, detailing essential skills (ACM, 2021).Country-specific vary; skills-based hiring under Executive Order 14110 practical over (US Office Personnel Management [OPM], Occupational Standards (NOS) Kingdom outlines detailed performance criteria life sciences (Unique Registration Number [URN] COGBIO-05), applicable specialized Standards, 2018).The scientist plays synthesizer knowledge, patterns large, disparate domains, clinical, genomic, environmental (Hassan Within do employ variety breadth contribution extends beyond reinforcement learning, Bayesian networks, simulation-based approaches, others.Reinforcement type algorithm learns sequences maximizing cumulative rewards, (Coronato account differences between classic RL following Markov assumption future state system depends its current (Kuznetsov 2010). suggesting adaptation model. dynamic strategies patient's response ongoing treatments. continuously adjust dosages chemotherapy minimize effectiveness (Eckardt Tempo, novel framework screening, context breast cancer. Tempo policy, combined model, outperforms practices detection adapted screening preferences. It improves overscreening (Yala allows time, adaptive new emerges. tailors assessments profiles, enhancing precision.Data probabilistic graphical represent set variables conditional dependencies. likelihood observed data. setting, sources-clinical biomarkers, history-to estimate probabilities uncertainty quantifications, helping doctors informed cases ambiguity (Polotskaya Huehn al developed digital model relevant head neck squamous cell carcinoma (HNSCC). Validation showed guides immunotherapy decisions, 84% concordance (Cohen's κ = 0.505, p 0.009) when actual 25 created physician's patient.Simulation-based enable virtual scenarios evaluate outcomes. simulating strategies, explore consequences before applying simulations options, long-term effects profiles (Nave, Federov (2020), method optimizing Treating Fields (TTFields) brain tumors. TTFields, delivered through transducer arrays skin, inhibit growth, distribution varying array placement, anatomy, characteristics. Incorporating expected physician TTFields ultimately improving outcomes.In addition dose optimization amount timing radiation. aim balance efficacy minimizing side effects, involve toxic agents. adjusting dosing schedules metabolism characteristics, duration frequency increase probability success without compromising (Bräutigam, emergence anti-tumor complicates this, creating urgent optimized dose-schedule designs doses concurrently single trial (Chen recent deep (DL) led DL-based prediction models. Unlike traditional methods, DL automatically extracts features CT, MRI, PET scans map values, guiding final distribution. distributions anatomical prescriptions (Jiang 2024).Multi-omics another important facet planning. combine genomics, transcriptomics, proteomics, metabolomics tumor. therapies. multi-omics might reveal just mutation interacts pathways, treatments, targeting metabolic aberration (Babu Snyder, Multi-omics offers view volumes pose analytical helps extracting omics advancing (Li Cai al. (2022), explored methods research, general-purpose task-specific approaches. benchmarked five Cell Line Encyclopedia, assessing classification, drug prediction, runtime efficiency. Their paper selecting encourages advance discovery treatments.Radiomics involves quantitative (e.g., scans) heterogeneity pathomics analyzes histopathological discernible pathologist alone. image-derived predictions select therapeutic Like (2024), radiopathomics classify stage I, II, III gastric Other researchers prognosis colorectal lung cancers, 2020a(Wang 2020b)). Radiomics situations available, offering non-invasive options (Gillies 2016;Brancato .Spatial biology technologies, GeoMx® (NanoString Technologies®) 1 CosMx™ 2 Visium® (10x Genomics®) 3 Xenium™ 4 revolutionizing profiling spatial context. mapping heterogeneity, microenvironment, cell-cell interactions, bulk offer. transcriptomics (spTx) 5 6 combining high-resolution RNA profiling, capturing cellular organization biomarker localization tissue samples (Cook HD 7 sub-cellular resolution, reconstruction morphology expression (Polanski 2024) .One notable spTx Despite faces intratumoral (ITH), tumour differently drugs. Using spTx, shows sensitivity tumor, core periphery. finds genetically identical cells depending location (Jimenez-Santos consider surrounding microenvironment. could addressing tumor's complexity, chances failure.Causal focuses determining cause-and-effect relationships, going correlation interventions Peter-Clark (PC) (Spirtes 1993) latent Gaussian causal (Cai SHapley Additive exPlanations (SHAP) Local Interpretable Agnostic Explanation (LIME) primarily explain correlations rather causation (Ladbury 2022).For language (LLM) impacting Non Small Lung (NSCLC), revealing potentially unexpected relationships smoking status having effect choice (Naik further, infe

Language: Английский

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