Core Viewpoint - The article discusses the revolutionary impact of Smart Spatial Omics (S2-omics) technology in biomedical research, which optimizes region selection for spatial omics experiments, enhancing molecular analysis while preserving tissue structure [2][19]. Group 1: Challenges in Spatial Omics - Spatial omics platforms like Xenium, Visium HD, and CosMx provide single-cell gene expression data but are costly, with sample costs reaching up to $7,000, and have limited tissue capture areas [6]. - Traditional region selection relies heavily on pathologists' subjective experience, leading to labor-intensive processes and variability in results across different laboratories [6][5]. Group 2: S2-omics Overview - S2-omics utilizes AI models to extract features from H&E stained images, simulating molecular heterogeneity to guide experimental design [8]. - The workflow consists of three steps: 1. Feature extraction from tissue images, segmenting them into 8μm×8μm superpixels to capture cellular morphology and tissue architecture [8]. 2. Automatic selection of regions of interest (ROI) based on a scoring system that balances coverage and diversity, allowing user-defined parameters [8]. 3. Prediction of molecular information for unmeasured areas based on selected regions, providing a comprehensive "virtual preview" of the tissue [9][11]. Group 3: Practical Applications - In a gastric cancer sample experiment, S2-omics selected a region covering 7 key tissue clusters, achieving prediction accuracies of 73.8% for cell types and 72.8% for community labels [13]. - In a colon cancer study, S2-omics covered 89.3% of the cells selected by experts while reducing blank areas, thus capturing critical structures more effectively [14]. - For kidney samples, S2-omics optimized the layout of views, successfully capturing glomeruli structures and enhancing data continuity and interpretability [15]. Group 4: Flexibility and Efficiency - S2-omics allows users to specify "positive priors" (e.g., focusing on tumor clusters) or "negative priors" (e.g., ignoring muscle areas), adjusting selection strategies accordingly [16]. - The system can automatically determine the optimal number of regions needed, as demonstrated in breast cancer samples where it identified two 2mm×2mm regions sufficient for capturing heterogeneity [17]. Group 5: Implications for Research - The introduction of S2-omics marks a significant step towards standardization and reproducibility in spatial omics experiments, reducing costs and subjective bias while empowering subsequent experimental designs through virtual predictions [19].

Financial Performance - Bruker's Q1 2025 revenues increased by $79.7 million, representing an 11% growth compared to Q1 2024[6] - The company's constant-exchange rate (CER) growth was +12.5%[6] - Organic revenue change increased by +2.9%, with Bruker Scientific Instruments (BSI) up +5.1% and Bruker Energy & Supercon Technologies (BEST) down -17.7%[6] - Acquisitions contributed +9.6% to revenue growth, while foreign exchange (FX) had a -1.5% headwind[6] - Non-GAAP EPS decreased by -11.3% to $0.47, compared to $0.53 in Q1 2024[6] Segment Performance - Bruker BIOSPIN Group experienced mid-teens % CER revenue growth, driven by preclinical imaging and lab automation[12] - Bruker CALID Group saw mid-20s % CER revenue growth, with strong performance in Life Science Mass Spectrometry and Microbiology & Infection Diagnostics[12] - Bruker NANO Group's CER revenue increased by high single digits %, aided by NanoString acquisition[18] - Bruker Energy & Supercon Technologies (BEST) segment experienced a high teens % decline in CER revenue, primarily due to softness in superconductors for clinical MRI[18] Financial Outlook - The company projects FY 2025 revenue between $3.48 billion and $3.55 billion, representing a reported revenue growth of +3.5% to +5.5%[45] - Organic revenue growth for FY 2025 is expected to be between +0% and +2%[45] - Non-GAAP EPS for FY 2025 is projected to be between $2.40 and $2.48, with a reported EPS growth of +0% to +3%[45]