Field Guide to Traction Force Microscopy.

Introduction: Traction force microscopy (TFM) is a widely used technique to measure cell contractility on compliant substrates that mimic the stiffness of human tissues. For every step in a TFM workflow, users make choices which impact the quantitative results, yet many times the rationales and consequences for making these decisions are unclear. We have found few papers which show the complete experimental and mathematical steps of TFM, thus obfuscating the full effects of these decisions on the final output. Methods: Therefore, we present this "Field Guide" with the goal to explain the mathematical basis of common TFM methods to practitioners in an accessible way. We specifically focus on how errors propagate in TFM workflows given specific experimental design and analytical choices. Results: We cover important assumptions and considerations in TFM substrate manufacturing, substrate mechanical properties, imaging techniques, image processing methods, approaches and parameters used in calculating traction stress, and data-reporting strategies. Conclusions: By presenting a conceptual review and analysis of TFM-focused research articles published over the last two decades, we provide researchers in the field with a better understanding of their options to make more informed choices when creating TFM workflows depending on the type of cell being studied. With this review, we aim to empower experimentalists to quantify cell contractility with confidence. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-024-00801-6.

Microenvironmental network of clonal CXCL13+CD4+ T cells and Tregs in pemphigus chronic blisters.

BACKGROUNDPemphigus, a rare autoimmune bullous disease mediated by antidesmoglein autoantibodies, can be controlled with systemic medication like rituximab and high-dose systemic corticosteroids combined with immunosuppressants. However, some patients continue to experience chronically recurrent blisters in a specific area and require long-term maintenance systemic therapy.METHODSSkin with chronic blisters was obtained from patients with pemphigus. Immunologic properties of the skin were analyzed by immunofluorescence staining, bulk and single-cell RNA and TCR sequencing, and a highly multiplex imaging technique known as CO-Detection by indEXing (CODEX). Functional analyses were performed by flow cytometry and bulk RNA-Seq using peripheral blood from healthy donors. Intralesional corticosteroid was injected into patient skin, and changes in chronically recurrent blisters were observed.RESULTSWe demonstrated the presence of skin tertiary lymphoid structures (TLSs) with desmoglein-specific B cells in chronic blisters from patients with pemphigus. In the skin TLSs, CD4+ T cells predominantly produced CXCL13. These clonally expanded CXCL13+CD4+ T cells exhibited features of activated Th1-like cells and downregulated genes associated with T cell receptor-mediated signaling. Tregs are in direct contact with CXCL13+CD4+ memory T cells and increased CXCL13 production of CD4+ T cells through IL-2 consumption and TGF-ß stimulation. Finally, intralesional corticosteroid injection improved chronic blisters and reduced skin TLSs in patients with pemphigus.CONCLUSIONThrough this study we conclude that skin TLSs are associated with the persistence of chronically recurrent blisters in patients with pemphigus, and the microenvironmental network involving CXCL13+CD4+ T cells and Tregs within these structures plays an important role in CXCL13 production.TRIAL REGISTRATIONClinicalTrials.gov NCT04509570.FUNDINGThis work was supported by National Research Foundation of South Korea (NRF-2021R1C1C1007179) and Korea Drug Development Fund, which is funded by Ministry of Science and ICT; Ministry of Trade, Industry, and Energy; and Ministry of Health and Welfare (grant RS-2022-00165917).

Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.

MOTIVATION: Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. RESULTS: Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. AVAILABILITY AND IMPLEMENTATION: The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers.

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

Multi-omic and spatial dissection of immunotherapy response groups in non-small cell lung cancer.

The composition and activation status of the cellular milieu contained within the tumour microenvironment (TME) is becoming increasingly recognized as a driving factor for immunotherapy response. Here, we employed multiplex immunohistochemistry (mIHC), and digital spatial profiling (DSP) to capture the targeted immune proteome and transcriptome of tumour and TME compartments from an immune checkpoint inhibitor (ICI)-treated (n = 41) non-small cell lung cancer (NSCLC) patient cohort. We demonstrate by mIHC that the interaction of CD68+ macrophages with PD1+ , FoxP3+ cells is enriched in ICI refractory tumours (p = 0.012). Patients responsive to ICI therapy expressed higher levels of IL2 receptor alpha (CD25, p = 0.028) within their tumour compartments, which corresponded with increased IL2 mRNA (p = 0.001) within their stroma. In addition, stromal IL2 mRNA levels positively correlated with the expression of pro-apoptotic markers cleaved caspase 9 (p = 2e-5 ) and BAD (p = 5.5e-4 ) and negatively with levels of memory marker, CD45RO (p = 7e-4 ). Immuno-inhibitory markers CTLA-4 (p = 0.021) and IDO-1 (p = 0.023) were suppressed in ICI-responsive patients. Tumour expression of CD44 was depleted in the responsive patients (p = 0.02), while higher stromal expression of one of its ligands, SPP1 (p = 0.008), was observed. Cox survival analysis also indicated tumour CD44 expression was associated with poorer prognosis (hazard ratio [HR] = 1.61, p = 0.01), consistent with its depletion in ICI-responsive patients. Through multi-modal approaches, we have dissected the characteristics of NSCLC immunotherapy treatment groups and provide evidence for the role of several markers including IL2, CD25, CD44 and SPP1 in the efficacy of current generations of ICI therapy.

Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens.

Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of cellular environments at subcellular resolution. However, identifying and characterizing disease-relevant microenvironments from these rich datasets is challenging. Here we show that a graph neural network that leverages spatial protein profiles in tissue specimens to model tumour microenvironments as local subgraphs captures distinctive cellular interactions associated with differential clinical outcomes. We applied this spatial cellular-graph strategy to specimens of human head-and-neck and colorectal cancers assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and with patient survival after treatment. The graph deep learning model was substantially more accurate in predicting patient outcomes than deep learning approaches that model spatial data on the basis of the local composition of cell types, and it generated insights into the effect of the spatial compartmentalization of tumour cells and granulocytes on patient prognosis. Local graphs may also aid in the analysis of disease-relevant motifs in histology samples characterized via spatial transcriptomics and other -omics techniques.

4-bit adhesion logic enables universal multicellular interface patterning.

Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions1,2. The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning3,4. Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems5-8. However, our ability to engineer multicellular interface patterns2,9 is still very limited, as synthetic cell-cell adhesion toolkits and suitable patterning algorithms are underdeveloped5,7,10-13. Here we introduce a synthetic cell-cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials5-8,14. Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems3,5.

Engineering and modeling of multicellular morphologies and patterns.

Synthetic multicellular (MC) systems have the capacity to increase our understanding of biofilms and higher organisms, and to serve as engineering platforms for developing complex products in the areas of medicine, biosynthesis and smart materials. Here we provide an interdisciplinary perspective and review on emerging approaches to engineer and model MC systems. We lay out definitions for key terms in the field and identify toolboxes of standardized parts which can be combined into various MC algorithms to achieve specific outcomes. Many essential parts and algorithms have been demonstrated in some form. As key next milestones for the field, we foresee the improvement of these parts and their adaptation to more biological systems, the demonstration of more complex algorithms, the advancement of quantitative modeling approaches and compilers to support rational MC engineering, and implementation of MC engineering for practical applications.

Regulation of epithelial migration by epithelial cell adhesion molecule requires its Claudin-7 interaction domain.

Epithelial cell adhesion molecule (EpCAM) is a glycoprotein on the surface of epithelial cells that is essential for intestinal epithelial integrity and expressed at high levels in many epithelial derived cancers and circulating tumor cells. Here we show the effect of EpCAM levels on migration of Madin-Darby-Canine Kidney (MDCK) epithelial cells. MDCK cells depleted of EpCAM show increased activation of extracellular signal-regulated kinase (ERK) and of myosin, and increased cell spreading and epithelial sheet migration into a gap. In contrast, over-expression of EpCAM inhibits ERK and myosin activation, and slows epithelial sheet migration. Loss of EpCAM is rescued by EpCAM-YFP mutated in the extracellular domain required for cis-dimerization whereas EpCAM-YFP with a mutation that inhibits Claudin-7 interaction cannot rescue increased ERK, myosin activation, and increased migration in EpCAM-depleted cells. In summary, these results indicate that interaction of EpCAM and Claudin-7 at the cell surface negatively regulates epithelial migration by inhibiting ERK and actomyosin contractility.

Correction: LudusScope: Accessible Interactive Smartphone Microscopy for Life-Science Education.

[This corrects the article DOI: 10.1371/journal.pone.0162602.].

LudusScope: Accessible Interactive Smartphone Microscopy for Life-Science Education.

For centuries, observational microscopy has greatly facilitated biology education, but we still cannot easily and playfully interact with the microscopic world we see. We therefore developed the LudusScope, an accessible, interactive do-it-yourself smartphone microscopy platform that promotes exploratory stimulation and observation of microscopic organisms, in a design that combines the educational modalities of build, play, and inquire. The LudusScope's touchscreen and joystick allow the selection and stimulation of phototactic microorganisms such as Euglena gracilis with light. Organismal behavior is tracked and displayed in real time, enabling open and structured game play as well as scientific inquiry via quantitative experimentation. Furthermore, we used the Scratch programming language to incorporate biophysical modeling. This platform is designed as an accessible, low-cost educational kit for easy construction and expansion. User testing with both teachers and students demonstrates the educational potential of the LudusScope, and we anticipate additional synergy with the maker movement. Transforming observational microscopy into an interactive experience will make microbiology more tangible to society, and effectively support the interdisciplinary learning required by the Next Generation Science Standards.

Microfluidic assembly kit based on laser-cut building blocks for education and fast prototyping.

Here, we present an inexpensive rapid-prototyping method that allows researchers and children to quickly assemble multi-layered microfluidic devices from easily pre-fabricated building blocks. We developed low-cost (<$2) kits based on laser-cut acrylic building block pieces and double-sided tape that allow users to generate water droplets in oil, capture living cells, and conduct basic phototaxis experiments. We developed and tested a 90-min lesson plan with children aged 12-14 yr and provide here the instructions for teachers to replicate these experiments and lessons. All parts of the kit are easy to make or order. We propose to use such easy to fabricate kits in labs with no access to current microfluidic tools as well as in classroom environments to get exposure to the powerful techniques of microfluidics.