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"Histocytochemistry"
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The chemical reactions of life : from metabolism to photosynthesis
Presents an introduction to the biochemistry, describes the history of the science, and discusses chemical reactions found in plants and animals.
Book
Data-efficient and weakly supervised computational pathology on whole-slide images
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.
A data-efficient and interpretable deep-learning method for the multi-class classification of whole-slide images that relies only on slide-level labels is applied to the detection of lymph node metastasis and to cancer subtyping.
Journal Article
Validating Whole Slide Imaging for Diagnostic Purposes in Pathology: Guideline from the College of American Pathologists Pathology and Laboratory Quality Center
Context. —There is increasing interest in using whole slide imaging (WSI) for diagnostic purposes (primary and/or consultation). An important consideration is whether WSI can safely replace conventional light microscopy as the method by which pathologists review histologic sections, cytology slides, and/or hematology slides to render diagnoses. Validation of WSI is crucial to ensure that diagnostic performance based on digitized slides is at least equivalent to that of glass slides and light microscopy. Currently, there are no standard guidelines regarding validation of WSI for diagnostic use. Objective. —To recommend validation requirements for WSI systems to be used for diagnostic purposes. Design. —The College of American Pathologists Pathology and Laboratory Quality Center convened a nonvendor panel from North America with expertise in digital pathology to develop these validation recommendations. A literature review was performed in which 767 international publications that met search term requirements were identified. Studies outside the scope of this effort and those related solely to technical elements, education, and image analysis were excluded. A total of 27 publications were graded and underwent data extraction for evidence evaluation. Recommendations were derived from the strength of evidence determined from 23 of these published studies, open comment feedback, and expert panel consensus. Results. —Twelve guideline statements were established to help pathology laboratories validate their own WSI systems intended for clinical use. Validation of the entire WSI system, involving pathologists trained to use the system, should be performed in a manner that emulates the laboratory's actual clinical environment. It is recommended that such a validation study include at least 60 routine cases per application, comparing intraobserver diagnostic concordance between digitized and glass slides viewed at least 2 weeks apart. It is important that the validation process confirm that all material present on a glass slide to be scanned is included in the digital image. Conclusions. —Validation should demonstrate that the WSI system under review produces acceptable digital slides for diagnostic interpretation. The intention of validating WSI systems is to permit the clinical use of this technology in a manner that does not compromise patient care.
Journal Article
A pathology foundation model for cancer diagnosis and prognosis prediction
Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task
1
,
2
. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations
3
. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.
A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.
Journal Article
Mass spectrometry imaging with high resolution in mass and space
Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (
R
= 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level.
Journal Article
Historical Perspective: Models of Parkinson’s Disease
Parkinson’s disease (PD) is the most common movement disorder with motor and nonmotor signs. The current therapeutic regimen for PD is mainly symptomatic as the etio-pathophysiology has not been fully elucidated. A variety of animal models has been generated to study different aspects of the disease for understanding the pathogenesis and therapeutic development. The disease model can be generated through neurotoxin-based or genetic-based approaches in a wide range of animals such as non-human primates (NHP), rodents, zebrafish, Caenorhabditis (C.) elegans, and drosophila. Cellular-based disease model is frequently used because of the ease of manipulation and suitability for large-screen assays. In neurotoxin-induced models, chemicals such as 6-hydroxydopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), rotenone, and paraquat are used to recapitulate the disease. Genetic manipulation of PD-related genes, such as α-Synuclein(SNCA), Leucine-rich repeat kinase 2 (LRRK2), Pten-Induced Kinase 1 (PINK1), Parkin(PRKN), and Protein deglycase (DJ-1) Are used in the transgenic models. An emerging model that combines both genetic- and neurotoxin-based methods has been generated to study the role of the immune system in the pathogenesis of PD. Here, we discuss the advantages and limitations of the different PD models and their utility for different research purposes.
Journal Article
Multi-class texture analysis in colorectal cancer histology
Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.
Journal Article
Proceedings of the 39th Congress Italian Society of Histochemistry - 14-16 June 2023, Vulcano Island, Italy
Proceedings of the 39th Congress of the Italian Society of Histochemistry (SII) - 14-16 June 2023, Hotel Therasia Resort, Vulcano Island, Italy.Proceedings of the 39th Congress of the Italian Society of Histochemistry (SII) - 14-16 June 2023, Hotel Therasia Resort, Vulcano Island, Italy.
Journal Article
How Hidden Can Malaria Be in Pregnant Women? Diagnosis by Microscopy, Placental Histology, Polymerase Chain Reaction and Detection of Histidine-Rich Protein 2 in Plasma
Background. Accurate diagnosis of malaria infection during pregnancy remains challenging because of low parasite densities and placental sequestration of Plasmodium falciparum. The performance of different methods to detect P. falciparum in pregnancy and the clinical relevance of undetected infections were evaluated. Methods. P. falciparum infections were assessed in 272 Mozambican women at delivery by microscopy, placental histology, quantitative polymerase chain reaction (qPCR) and detection of histidine-rich protein 2 (HRP2) in plasma by enzyme-linked immunosorbent assay (ELISA) and a rapid diagnostic test (RDT). Association between infection and delivery outcomes was determined. Results. Among the 122 women qPCR-positive for P. falciparum in peripheral and/or placental blood samples, 87 (71.3%) did not receive a positive diagnosis by peripheral microscopy, 75 (61.5%) by HRP2 ELISA, and 74 (60.7%) by HRP2 RDT in plasma. Fifty-seven of the 98 qPCR-positive placental infections (58.2%) were not detected by histology. Women who were qPCR-positive but negative in their peripheral blood by microscopy or HRP2 RDT in plasma (n = 62) were at increased risk of anemia, compared with negative women (n = 141; odds ratio, 2.03; 95% confidence interval, 1.07—3.83; P = .029). Conclusions. Microscopy, placental histology and HRP2-based plasma diagnostic methods fail to identify the majority of the P. falciparum infections detected by qPCR in peripheral and placental blood. Undetected infections were associated with maternal anemia, highlighting the urgent need for more accurate malaria diagnostic tools for pregnant women to avoid the negative clinical impact that hidden infections can have during pregnancy. Clinical Trials Registration. NCT00209781.
Journal Article
Tumor-secreted dickkopf2 accelerates aerobic glycolysis and promotes angiogenesis in colorectal cancer
Angiogenesis is a fundamental process that involves in tumor progression and metastasis. Vascular endothelial growth factor (VEGF) family and their receptors are identified as the most prominent regulators of angiogenesis. However, the clinical efficacy of anti-VEGF/VEGFR therapy is not ideal, prompting the needs to further understand mechanisms behind tumor angiogenesis. Here, we found that Dickkopf associated protein 2 (DKK2), a secretory protein highly expressed in metastatic colorectal cancer tissues, could stimulate angiogenesis via a classic VEGF/VEGFR independent pathway.
: DKK2 was screened out from microarray data analyzing gene expression profiles of eight pairs of non-metastatic and metastatic human colorectal cancer (CRC) tissues. Immunofluorescence histochemical staining (IHC) was used to detect the expression of DKK2 and angiogenesis in CRC tissues. Chicken chorioallantoic membrane (CAM) assay and Human umbilical vein endothelial cells (HUVEC) tubule formation assay was used for
and in
angiogenesis study, respectively. Lactate and glucose concentration in the culture medium was measured by enzyme-linked immunosorbent assay (ELISA). Luciferase reporter assay was used to verify the interaction between miR-493-5p and the 3'UTR of DKK2.
: DKK2 could stimulate angiogenesis via accelerating the aerobic glycolysis of CRC cells, through which lactate is produced from glucose and accumulated in tumor microenvironment. Lactate functions as the final executor of DDK2 to stimulate tube formation of endothelial cells, and blockage of lactate secretion by lactate transporter (MCT) inhibitors dramatically neutralize the progression and metastasis of CRC both
and
. DKK2 could cooperate with lipoprotein receptor-related protein 6, which is required for glucose uptake, and activated the downstream mTOR signal pathway to accelerate lactate secretion. In addition, the expression of DKK2 is switched on via the demethylation of miR-493-5p, which allows the dissociated of miR-493-5p from the 3'-UTRs of DKK2 and initiates its stimulatory role on CRC progression in an autocrine or paracrine manner.
: DKK2 promotes tumor metastasis and angiogenesis through a novel VEGF-independent, but energy metabolism related pathway. DKK2 might be a potential anti-angiogenic target in clinical treatment for the advanced CRC patients.
Journal Article