Spatial proteomics of human diabetic kidney disease, from health to class III.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. METHODS: Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). RESULTS: These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. CONCLUSIONS/INTERPRETATION: Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

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.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging.

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

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.

Activating Immune Recognition in Pancreatic Ductal Adenocarcinoma via Autophagy Inhibition, MEK Blockade, and CD40 Agonism.

BACKGROUND AND AIMS: Patients with pancreatic ductal adenocarcinoma (PDA) have not yet benefitted from the revolution in cancer immunotherapy due in large part to a dominantly immunosuppressive tumor microenvironment. MEK inhibition combined with autophagy inhibition leads to transient tumor responses in some patients with PDA. We examined the functional effects of combined MEK and autophagy inhibition on the PDA immune microenvironment and the synergy of combined inhibition of MEK and autophagy with CD40 agonism (aCD40) against PDA using immunocompetent model systems. METHODS: We implanted immunologically "cold" murine PDA cells orthotopically in wide type C57BL/6J mice. We administered combinations of inhibitors of MEK1/2, inhibitors of autophagy, and aCD40 and measured anticancer efficacy and immune sequelae using mass cytometry and multiplexed immunofluorescence imaging analysis to characterize the tumor microenvironment. We also used human and mouse PDA cell lines and human macrophages in vitro to perform functional assays to elucidate the cellular effects induced by the treatments. RESULTS: We find that coinhibition of MEK (using cobimetinib) and autophagy (using mefloquine), but not either treatment alone, activates the STING/type I interferon pathway in tumor cells that in turn activates paracrine tumor associated macrophages toward an immunogenic M1-like phenotype. This switch is further augmented by aCD40. Triple therapy (cobimetinib + mefloquine + aCD40) achieved cytotoxic T-cell activation in an immunologically "cold" mouse PDA model, leading to enhanced antitumor immunity. CONCLUSIONS: MEK and autophagy coinhibition coupled with aCD40 invokes immune repolarization and is an attractive therapeutic approach for PDA immunotherapy development.

Single-Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis.

Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial-mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single-cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal-to-endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single-cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.

Biodegradable fluorescent nanoparticles for endoscopic detection of colorectal carcinogenesis.

Early and comprehensive endoscopic detection of colonic dysplasia - the most clinically significant precursor lesion to colorectal adenocarcinoma - provides an opportunity for timely, minimally-invasive intervention to prevent malignant transformation. Here, the development and evaluation of biodegradable near-infrared fluorescent silica nanoparticles (FSN) is described that have the potential to improve adenoma detection during fluorescence-assisted white-light colonoscopic surveillance in rodent and human-scale models of colorectal carcinogenesis. FSNs are biodegradable (t1/2 of 2.7 weeks), well-tolerated, and enable detection and delineation of adenomas as small as 0.5 mm2 with high tumor-to-background ratios. Furthermore, in the human-scale, APC 1311/+ porcine model, the clinical feasibility and benefit of using FSN-guided detection of colorectal adenomas using video-rate fluorescence-assisted white-light endoscopy is demonstrated. Since nanoparticles of similar size (e.g., 100-150-nm) or composition (i.e., silica, silica/gold hybrid) have already been successfully translated to the clinic, and, clinical fluorescent/white light endoscopy systems are becoming more readily available, there is a viable path towards clinical translation of the proposed strategy for early colorectal cancer detection and prevention in high-risk patients.

Engineering high-affinity PD-1 variants for optimized immunotherapy and immuno-PET imaging.

Signaling through the immune checkpoint programmed cell death protein-1 (PD-1) enables tumor progression by dampening antitumor immune responses. Therapeutic blockade of the signaling axis between PD-1 and its ligand programmed cell death ligand-1 (PD-L1) with monoclonal antibodies has shown remarkable clinical success in the treatment of cancer. However, antibodies have inherent limitations that can curtail their efficacy in this setting, including poor tissue/tumor penetrance and detrimental Fc-effector functions that deplete immune cells. To determine if PD-1:PD-L1-directed immunotherapy could be improved with smaller, nonantibody therapeutics, we used directed evolution by yeast-surface display to engineer the PD-1 ectodomain as a high-affinity (110 pM) competitive antagonist of PD-L1. In contrast to anti-PD-L1 monoclonal antibodies, high-affinity PD-1 demonstrated superior tumor penetration without inducing depletion of peripheral effector T cells. Consistent with these advantages, in syngeneic CT26 tumor models, high-affinity PD-1 was effective in treating both small (50 mm(3)) and large tumors (150 mm(3)), whereas the activity of anti-PD-L1 antibodies was completely abrogated against large tumors. Furthermore, we found that high-affinity PD-1 could be radiolabeled and applied as a PET imaging tracer to efficiently distinguish between PD-L1-positive and PD-L1-negative tumors in living mice, providing an alternative to invasive biopsy and histological analysis. These results thus highlight the favorable pharmacology of small, nonantibody therapeutics for enhanced cancer immunotherapy and immune diagnostics.

Local estrogen axis in the human bone microenvironment regulates estrogen receptor-positive breast cancer cells.

BACKGROUND: Approximately 70% of all breast cancers express the estrogen receptor, and are regulated by estrogen. While the ovaries are the primary source of estrogen in premenopausal women, most breast cancer is diagnosed following menopause, when systemic levels of this hormone decline. Estrogen production from androgen precursors is catalyzed by the aromatase enzyme. Although aromatase expression and local estrogen production in breast adipose tissue have been implicated in the development of primary breast cancer, the source of estrogen involved in the regulation of estrogen receptor-positive (ER+) metastatic breast cancer progression is less clear. METHODS: Bone is the most common distant site of breast cancer metastasis, particularly for ER+ breast cancers. We employed a co-culture model using trabecular  bone tissues obtained from total hip replacement (THR) surgery specimens to study ER+ and estrogen receptor-negative (ER-) breast cancer cells within the human bone microenvironment. Luciferase-expressing ER+ (MCF-7, T-47D, ZR-75) and ER- (SK-BR-3, MDA-MB-231, MCF-10A) breast cancer cells were cultured directly on bone tissue fragments or in bone tissue-conditioned media, and monitored over time with bioluminescence imaging (BLI). Bone tissue-conditioned media were generated in the presence vs. absence of aromatase inhibitors, and testosterone. Bone tissue fragments were analyzed for aromatase expression by immunohistochemistry. RESULTS: ER+ breast cancer cells were preferentially sustained in co-cultures with bone tissues and bone tissue-conditioned media relative to ER- cells. Bone fragments analyzed by immunohistochemistry revealed expression of the aromatase enzyme. Bone tissue-conditioned media generated in the presence of testosterone had increased estrogen levels and heightened capacity to stimulate ER+ breast cancer cell proliferation. Pretreatment of cultured bone tissues with aromatase inhibitors, which inhibited estrogen production, reduced the capacity of conditioned media to stimulate ER+ cell proliferation. CONCLUSIONS: These results suggest that a local estrogen signaling axis regulates ER+ breast cancer cell viability and proliferation within the bone metastatic niche, and that aromatase inhibitors modulate this axis. Although endocrine therapies are highly effective in the treatment of ER+ breast cancer, resistance to these treatments reduces their efficacy. Characterization of estrogen signaling networks within the bone microenvironment will identify new strategies for combating metastatic progression and endocrine resistance.

Novel Radiotracer for ImmunoPET Imaging of PD-1 Checkpoint Expression on Tumor Infiltrating Lymphocytes.

Immune checkpoint signaling through the programmed death 1 (PD-1) axis to its ligand (PD-L1) significantly dampens anti-tumor immune responses. Cancer patients treated with checkpoint inhibitors that block this suppressive signaling have exhibited objective response rates of 20-40% for advanced solid tumors, lymphomas, and malignant melanomas. This represents a tremendous advance in cancer treatment. Unfortunately, all patients do not respond to immune checkpoint blockade. Recent findings suggest that patients with tumor infiltrating lymphocytes (TILs) expressing PD-1 may be most likely to respond to αPD-1/PD-L1 checkpoint inhibitors. There is a compelling need for diagnostic and prognostic imaging tools to assess the PD-1 status of TILs in vivo. Here we have developed a novel immunoPET tracer to image PD-1 expressing TILs in a transgenic mouse model bearing melanoma. A (64)Cu labeled anti-mouse antibody (IgG) PD-1 immuno positron emission tomography (PET) tracer was developed to detect PD-1 expressing murine TILs. Quality control of the tracer showed >95% purity by HPLC and >70% immunoreactivity in an in vitro cell binding assay. ImmunoPET scans were performed over 1-48 h on Foxp3+.LuciDTR4 mice bearing B16-F10 melanoma tumors. Mice receiving anti-PD-1 tracer (200 ± 10 µCi/10-12 µg/200 µL) revealed high tracer uptake in lymphoid organs and tumors. BLI images of FoxP3(+) CD4(+) Tregs known to express PD-1 confirmed lymphocyte infiltration of tumors at the time of PET imaging. Biodistribution measurements performed at 48 h revealed a high (11×) tumor to muscle uptake ratio of the PET tracer (p < 0.05). PD-1 tumors exhibited 7.4 ± 0.7%ID/g tracer uptake and showed a 2× fold signal decrease when binding was blocked by unlabeled antibody. To the best of our knowledge this data is the first report to image PD-1 expression in living subjects with PET. This radiotracer has the potential to assess the prognostic value of PD-1 in preclinical models of immunotherapy and may ultimately aid in predicting response to therapies targeting immune checkpoints.

On-command drug release from nanochains inhibits growth of breast tumors.

PURPOSE: To evaluate the ability of radiofrequency (RF)-triggered drug release from a multicomponent chain-shaped nanoparticle to inhibit the growth of an aggressive breast tumor. METHODS: A two-step solid phase chemistry was employed to synthesize doxorubicin-loaded nanochains, which were composed of three iron oxide nanospheres and one doxorubicin-loaded liposome assembled in a 100-nm-long linear nanochain. The nanochains were tested in the 4T1-LUC-GFP orthotopic mouse model, which is a highly aggressive breast cancer model. The 4T1-LUC-GFP cell line stably expresses firefly luciferase, which allowed the non-invasive in vivo imaging of tumor response to the treatment using bioluminescence imaging (BLI). RESULTS: Longitudinal BLI imaging showed that a single nanochain treatment followed by application of RF resulted in an at least 100-fold lower BLI signal compared to the groups treated with nanochains (without RF) or free doxorubicin followed by RF. A statistically significant increase in survival time of the nanochain-treated animals followed by RF (64.3 days) was observed when compared to the nanochain-treated group without RF (35.7 days), free doxorubicin-treated group followed by RF (38.5 days), and the untreated group (30.5 days; n=5 animals per group). CONCLUSIONS: These studies showed that the combination of RF and nanochains has the potential to effectively treat highly aggressive cancers and prolong survival.

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement.

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging.

BACKGROUND: Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. METHODS: The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. RESULTS: In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. CONCLUSIONS: Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

Discovery and generalization of tissue structures from spatial omics data.

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

A single-cell atlas of IL-23 inhibition in cutaneous psoriasis distinguishes clinical response.

Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of interleukin-23 (IL-23) signaling, but the genetic mechanisms underlying clinical responses remain poorly understood. Using single-cell transcriptomics, we profiled immune cells isolated from lesional psoriatic skin before and during IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was strongly attenuated. In contrast, poorly responsive patients were distinguished by persistent activation of IL-17-producing T (T17) cells, a mechanism distinct from alternative cytokine signaling or resistance isolated to epidermal keratinocytes. Even in IL-23 blockade-responsive patients, we detected a recurring set of recalcitrant, disease-specific transcriptional abnormalities. This irreversible immunological state may necessitate ongoing IL-23 inhibition. Spatial transcriptomic analyses also suggested that successful IL-23 blockade requires dampening of >90% of IL-17-induced response in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. Collectively, our data establish a patient-level paradigm for dissecting responses to immunomodulatory treatments.

Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy.

Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITF+SPARCL1+ and CENPF+ melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.

Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease.

Hepatocellular carcinoma (HCC) frequently recurs from minimal residual disease (MRD), which persists after therapy. Here, we identified mechanisms of persistence of residual tumor cells using post-chemoembolization human HCC (n = 108 patients, 1.07 million cells) and a transgenic mouse model of MRD. Through single-cell high-plex cytometric imaging, we identified a spatial neighborhood within which PD-L1 + M2-like macrophages interact with stem-like tumor cells, correlating with CD8+ T cell exhaustion and poor survival. Further, through spatial transcriptomics of residual HCC, we showed that macrophage-derived TGFß1 mediates the persistence of stem-like tumor cells. Last, we demonstrate that combined blockade of Pdl1 and Tgfß excluded immunosuppressive macrophages, recruited activated CD8+ T cells and eliminated residual stem-like tumor cells in two mouse models: a transgenic model of MRD and a syngeneic orthotopic model of doxorubicin-resistant HCC. Thus, our spatial analyses reveal that PD-L1+ macrophages sustain MRD by activating the TGFß pathway in stem-like cancer cells and targeting this interaction may prevent HCC recurrence from MRD.

Combined near infrared photoacoustic imaging and ultrasound detects vulnerable atherosclerotic plaque.

Atherosclerosis is an inflammatory process resulting in the deposition of cholesterol and cellular debris, narrowing of the vessel lumen and clot formation. Characterization of the morphology and vulnerability of the lesion is essential for effective clinical management. Here, near-infrared auto-photoacoustic (NIRAPA) imaging is shown to detect plaque components and, when combined with ultrasound imaging, to differentiate stable and vulnerable plaque. In an ex vivo study of photoacoustic imaging of excised plaque from 25 patients, 88.2% sensitivity and 71.4% specificity were achieved using a clinically-relevant protocol. In order to determine the origin of the NIRAPA signal, immunohistochemistry, spatial transcriptomics and spatial proteomics were co-registered with imaging and applied to adjacent plaque sections. The highest NIRAPA signal was spatially correlated with bilirubin and associated blood-based residue and with the cytoplasmic contents of inflammatory macrophages bearing CD74, HLA-DR, CD14 and CD163 markers. In summary, we establish the potential to apply the NIRAPA-ultrasound imaging combination to detect vulnerable carotid plaque and a methodology for fusing molecular imaging with spatial transcriptomic and proteomic methods.

Combined near infrared photoacoustic imaging and ultrasound detects vulnerable atherosclerotic plaque.

Atherosclerosis is an inflammatory process resulting in the deposition of cholesterol and cellular debris, narrowing of the vessel lumen and clot formation. Characterization of the morphology and vulnerability of the lesion is essential for effective clinical management. Photoacoustic imaging has sufficient penetration and sensitivity to map and characterize human atherosclerotic plaque. Here, near infrared photoacoustic imaging is shown to detect plaque components and, when combined with ultrasound imaging, to differentiate stable and vulnerable plaque. In an ex vivo study of photoacoustic imaging of excised plaque from 25 patients, 88.2% sensitivity and 71.4% specificity were achieved using a clinically-relevant protocol. In order to determine the origin of the near-infrared auto-photoacoustic (NIRAPA) signal, immunohistochemistry, spatial transcriptomics and proteomics were applied to adjacent sections of the plaque. The highest NIRAPA signal was spatially correlated with bilirubin and associated blood-based residue and inflammatory macrophages bearing CD74, HLA-DR, CD14 and CD163 markers. In summary, we establish the potential to apply the NIRAPA-ultrasound imaging combination to detect vulnerable carotid plaque.