Simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues by multicolor quantum dots-based peptide probes

The accurate detection of multiplex collagen biomarkers is vital for diagnosing and treating various critical diseases such as tumors and fibrosis. Despite the attractive optical properties of quantum dots (QDs), it remains technically challenging to create stable and specific QDs-based probes for multiplex biological imaging. We report for the first time the construction of multi-color QDs-based peptide probes for the simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues. A bipeptide system composed of a glutathione (GSH) host peptide and a collagen-targeting guest peptide (CTP) has been developed, yielding CTP-QDs probes that exhibit exceptional luminescence stability when exposed to ultraviolet irradiation and mildly acidic conditions. The versatile bipeptide system allows for facile one-pot synthesis of high-quality multicolor CTP-QDs probes, exhibiting superior selectivity in targeting critical collagen biomarkers including denatured collagen, type I collagen, type II collagen, and type IV collagen. The multicolor CTP-QDs probes have demonstrated remarkable efficacy in simultaneously fingerprinting multiple collagen types in diverse connective tissues, irrespective of their status, whether affected by injury, diseases, or undergoing remodeling processes. The innovative multicolor CTP-QDs probes offer a robust toolkit for the multiplex fingerprinting of the collagen suprafamily, demonstrating significant potential in the diagnosis and treatment of collagen-related diseases.

Self-supervised deep learning for highly efficient spatial immunophenotyping

BACKGROUND

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets.

METHODS

This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model.

FINDINGS

With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression.

INTERPRETATION

By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data.

FUNDING

This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Cell-based optimization and characterization of genetically encoded location-based biosensors for Cdc42 or Rac activity

Rac (herein referring to the Rac family) and Cdc42 are Rho GTPases that regulate the formation of lamellipoda and filopodia, and are therefore crucial in processes such as cell migration. Relocation-based biosensors for Rac and Cdc42 have not been characterized well in terms of their specificity or affinity. In this study, we identify relocation sensor candidates for both Rac and Cdc42. We compared their (1) ability to bind the constitutively active Rho GTPases, (2) specificity for Rac and Cdc42, and (3) relocation efficiency in cell-based assays. Subsequently, the relocation efficiency was improved by a multi-domain approach. For Rac1, we found a sensor candidate with low relocation efficiency. For Cdc42, we found several sensors with sufficient relocation efficiency and specificity. These optimized sensors enable the wider application of Rho GTPase relocation sensors, which was showcased by the detection of local endogenous Cdc42 activity at assembling invadopodia. Moreover, we tested several fluorescent proteins and HaloTag for their influence on the recruitment efficiency of the Rho location sensor, to find optimal conditions for a multiplexing experiment. This characterization and optimization of relocation sensors will broaden their application and acceptance.

Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer

INTRODUCTION

Immune cells in the tumour microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterise the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC).

METHODS

We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8-Treg), B-cells, natural killer cells, natural killer T-cells, M1 macrophages (M1), CD163+ myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs).

RESULTS

CD4-Eff cells, CD8-Eff cells and M1 macrophages were the most abundant immune cells invading the tumour cell compartment and indicated a patient group with a favourable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells and pDCs) were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favourable impact was attenuated. In the multivariable Cox regression model, including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg-B-cells, CD8-Eff-cancer cells and B-cells-CD4-Treg) demonstrated positive prognostic impact, whereas short M2-M1 distances were prognostically unfavourable.

CONCLUSION

We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

UltraPlex Hapten-Based Multiplexed Fluorescent Immunohistochemistry

The UltraPlex method for multiplexed two-dimensional fluorescent immunohistochemistry is described, in which hapten tags conjugated to primary antibodies facilitate multiplexed imaging of four or more antigens per tissue section at once. Anti-hapten secondary antibodies labeled with fluorophores provide amplified signal for detection, which is accomplished using a standard fluorescent microscope or digital slide scanner. The protocol is rapid and straightforward and utilizes conventionally prepared tissue samples. The resulting staining is highly sensitive and specific, enabling high-resolution imaging of multiple cellular subtypes within tissue samples. Tumor cells and tumor-infiltrating lymphocytes are presented as examples. Multiple 4-plex-stained tissue samples can be digitally overlaid to create 8-plex (or more) high-content images, enabling visualization of distribution of complex cellular subtypes across tissues.

Multiplex Imaging of Rho GTPase Activities in Living Cells

Förster resonance energy transfer (FRET) biosensors are popular and useful for directly observing cellular signaling pathways in living cells. Until recently, multiplex imaging of genetically encoded FRET biosensors to simultaneously monitor several protein activities in one cell was limited due to a lack of spectrally compatible FRET pair of fluorescent proteins. With the recent development of miRFP series of near-infrared (NIR) fluorescent proteins, we are now able to extend the spectrum of FRET biosensors beyond blue-green-yellow into NIR. These new NIR FRET biosensors enable direct multiplex imaging together with commonly used cyan-yellow FRET biosensors. We describe herein a method to produce cell lines harboring two compatible FRET biosensors. We will then discuss how to directly multiplex-image these FRET biosensors in living cells. The approaches described herein are generally applicable to any combinations of genetically encoded, ratiometric FRET biosensors utilizing the cyan-yellow and NIR fluorescence.

Genetically encoded calcium indicators to probe complex brain circuit dynamics in vivo

Over the past two decades, genetically encoded calcium indicators (GECIs) have been used extensively to report intracellular calcium (Ca²⁺) dynamics in order to readout neuronal and network activity in living tissue. Single wavelength GECIs, such as GCaMP, have been widely adapted due to advances in dynamic range, sensitivity, and kinetics. Additionally, recent efforts in protein engineering have expanded the GECI color palette to enable direct optical interrogation of more complex circuit dynamics. Here, I discuss the engineering, application, and future directions of the most recently developed GECIs for in vivo neuroscience research.

Overview of Tissue Imaging Methods

The rapidly evolving fields of precision medicine and immuno-oncology are together driving an increasing need for detailed investigation of the tumor immune microenvironment (TIME) in a variety of solid tumors and hematologic neoplasms. The development of targeted therapies that may be efficacious in reprogramming the host immune response to recognize and eliminate tumor cells requires accurate identification of the various inflammatory cells and the spatial relationships between them within the TIME. While currently established techniques enable diagnostic pathologists to routinely interrogate for up to two protein markers and evaluate their expression by visual examination, there is a growing need to reliably query many more targets (i.e., multiplexing) simultaneously in a given tissue specimen, in order to more precisely characterize and distinguish the TIMEs between different tumor types, and between patients. Several technologies aimed at achieving these goals, including multiplex colorimetric immunohistochemistry (mCIHC), multiplex immunofluorescence (mIF), cyclic immunofluorescence (CycIF), multiplexed ion beam imaging (MIBI), codetection by indexing (CODEX), and digital spatial profiling (DSP), are discussed.

High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers

Biomarker assessments of tumor specimens is widely used in cancer research to audit tumor cell intrinsic as well as tumor cell extrinsic features including the diversity of immune, stromal, and mesenchymal cells. To comprehensively and quantitatively audit the tumor-immune microenvironment (TiME), we developed a novel multiplex immunohistochemistry (mIHC) platform and computational image processing workflow using a single formalin-fixed paraffin-embedded (FFPE) tissue section. Herein, we validated this platform using nine matched primary newly diagnosed and recurrent head and neck squamous cell carcinoma (HNSCC) sections sequentially subjected to immunodetection with a panel of 29 antibodies identifying malignant tumor cells, and 17 distinct leukocyte lineages and their functional states. Image cytometric analysis was applied to interpret chromogenic signals from digitally scanned and coregistered light microscopy-based images enabling identification and quantification of individual tumor cells, structural features, immune cell phenotypes and their functional state. In agreement with our previous study via a 12-plex imaging mIHC platform, myeloid-inflamed status in newly diagnosed primary tumors associated with significantly short progression free survival, independent of lymphoid-inflamed status. Spatial distribution of tumor and immune cell lineages in TiME was also examined and revealed statistically significant CD8⁺ T cell exclusion from tumor nests, whereas regulatory T cells and myeloid cells, when present in close proximity to tumor cells, highly associated with rapid cancer recurrence. These findings indicate presence of differential immune-spatial profiles in newly diagnosed and recurrent HNSCC, and establish the robustness of the 29-plex mIHC platform and associated analytics for quantitative analysis of single tissue sections revealing longitudinal TiME changes.

Phenotyping Multiple Subsets of Immune Cells In Situ in FFPE Tissue Sections: An Overview of Methodologies

The recent clinical success of new cancer immunotherapy agents and methods is driving the need to understand the role of immune cells in solid tissues, especially tumors. Immune cell phenotyping via flow cytometry, while a cornerstone of immunology, is not spatially resolved and cannot analyze immune cell subsets in situ in clinical biopsy sections or to determine their interrelationships. To address this problem, a number of methodologies have been developed in attempts to phenotype immune and other cells in images acquired from tissue sections and to assess their organization in the tumor and its microenvironment. This chapter review the staining and multiplex image analysis methods that have been developed for phenotyping immune and other cells in formalin-fixed, paraffin-embedded (FFPE) tissue sections.

Fluorescent imaging of cancerous tissues for targeted surgery

To maximize tumor excision and minimize collateral damage are the primary goals of cancer surgery. Emerging molecular imaging techniques have made "image-guided surgery" developed into "molecular imaging-guided surgery", which is termed as "targeted surgery" in this review. Consequently, the precision of surgery can be advanced from tissue-scale to molecule-scale, enabling "targeted surgery" to be a component of "targeted therapy". Evidence from numerous experimental and clinical studies has demonstrated significant benefits of fluorescent imaging in targeted surgery with preoperative molecular diagnostic screening. Fluorescent imaging can help to improve intraoperative staging and enable more radical cytoreduction, detect obscure tumor lesions in special organs, highlight tumor margins, better map lymph node metastases, and identify important normal structures intraoperatively. Though limited tissue penetration of fluorescent imaging and tumor heterogeneity are two major hurdles for current targeted surgery, multimodality imaging and multiplex imaging may provide potential solutions to overcome these issues, respectively. Moreover, though many fluorescent imaging techniques and probes have been investigated, targeted surgery remains at a proof-of-principle stage. The impact of fluorescent imaging on cancer surgery will likely be realized through persistent interdisciplinary amalgamation of research in diverse fields.

Optical imaging for stem cell differentiation to neuronal lineage

In regenerative medicine, the prospect of stem cell therapy holds great promise for the recovery of injured tissues and effective treatment of intractable diseases. Tracking stem cell fate provides critical information to understand and evaluate the success of stem cell therapy. The recent emergence of in vivo noninvasive molecular imaging has enabled assessment of the behavior of grafted stem cells in living subjects. In this review, we provide an overview of current optical imaging strategies based on cell- or tissue-specific reporter gene expression and of in vivo methods to monitor stem cell differentiation into neuronal lineages. These methods use optical reporters either regulated by neuron-specific promoters or containing neuron-specific microRNA binding sites. Both systems revealed dramatic changes in optical reporter imaging signals in cells differentiating into a neuronal lineage. The detection limit of weak promoters or reporter genes can be greatly enhanced by adopting a yeast GAL4 amplification system or an engineering-enhanced luciferase reporter gene. Furthermore, we propose an advanced imaging system to monitor neuronal differentiation during neurogenesis that uses in vivo multiplexed imaging techniques capable of detecting several targets simultaneously.