Article
Multidisciplinary Sciences
Yannik Severin, Benjamin D. Hale, Julien Mena, David Goslings, Beat M. Frey, Berend Snijder
Summary: Phenotypic plasticity in the immune system is influenced by donor age, gender, and blood pressure, resulting in distinct polarization and activation-associated phenotypes across immune cell classes. The morphology of T cells is associated with their transcriptional state, with inflammation-associated polarized T cell morphology and age-associated loss of mitochondria observed. This study reveals that immune cell phenotypes reflect both molecular and personal health information, providing new perspectives for deep immune phenotyping in individuals' health and disease.
Article
Chemistry, Analytical
Yao Wang, Jasmine Jin, Haoqing Jerry Wang, Lining Arnold Ju
Summary: Interactions between cancer cells and the extracellular matrix (ECM) in the tumor microenvironment are crucial for tumorigenesis, invasion, and metastasis. A novel acoustic force spectroscopy method using z-Movi((R)) technology was developed to quantify the cell-ECM adhesion strength in a fast, simple, and high-throughput manner. This platform enables functional assessment of cell adhesion molecules with high predictability and reproducibility.
Article
Chemistry, Analytical
Liyuan Liu, Haixia Liu, Xiaowen Huang, Xiaoli Liu, Chengyun Zheng
Summary: Cell culture is a significant life science technology. Compared to traditional two-dimensional cell culture, three-dimensional cell culture can mimic the natural environment and structure specificity of in vivo cell growth, making it a research hotspot. However, existing techniques for three-dimensional cell culture, such as the hanging drop method and spinner flask method, face challenges in obtaining uniform cell spheroids during high-throughput processes. In this study, a method for amplifying cell spheroids with the advantages of rapid scale-up and uniform morphology, with a survival rate of over 95%, is reported. This method is easy to operate and allows for convenient substance changes, suggesting its potential in cell-cell, cell-stroma, cell-organ interaction research, tissue engineering, and anti-cancer drug screening.
Article
Multidisciplinary Sciences
Tina Lasisi, Arslan A. Zaidi, Timothy H. Webster, Nicholas B. Stephens, Kendall Routch, Nina G. Jablonski, Mark D. Shriver
Summary: Researching the continuous variation in human scalp hair morphology is important across different fields, a high-throughput sample preparation protocol and a new Python package were introduced for imaging and processing of hair samples, demonstrating the benefits of quantifying hair morphology and discussing the relationship between cross-sectional morphology and curvature in depth.
SCIENTIFIC REPORTS
(2021)
Article
Biochemical Research Methods
Hannah K. Wayment-Steele, Wipapat Kladwang, Alexandra I. Strom, Jeehyung Lee, Adrien Treuille, Alex Becka, Rhiju Das
Summary: This study evaluates the accuracy of commonly used RNA structure modeling packages in ensemble-oriented prediction tasks and finds that packages with parameters derived through statistical learning achieve higher accuracy than those derived from thermodynamic experiments. Training a multitask model can improve performance in ensemble-based prediction tasks.
Article
Chemistry, Multidisciplinary
Jieke Jiang, Gary Shea, Prasansha Rastogi, Tom Kamperman, Cornelis H. Venner, Claas Willem Visser
Summary: This study demonstrates in-air polymerization of liquid jets as a novel platform to produce microparticles and microfibers with tunable size, shape, and composition at high throughput; the size, morphology, and local chemistry of micromaterials can be independently controlled, enabling rapid fabrication of stimuli-responsive Janus fibers and high-throughput printing of microlenses; this combination of rapid processing and tunability opens a new route toward applications of tailored micromaterials in various fields.
ADVANCED MATERIALS
(2021)
Review
Chemistry, Analytical
Majood Haddad, Alex N. Frickenstein, Stefan Wilhelm
Summary: Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level, discussing methods such as flow cytometry and inductively coupled plasma mass spectrometry, as well as their intersection in mass cytometry. The review also explores the challenges and opportunities in current single-cell analysis approaches and proposes directions for innovation in high-throughput analysis of nanoparticle-cell interactions.
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
(2023)
Review
Biochemical Research Methods
Joy A. Pai, Ansuman T. Satpathy
Summary: This Review discusses methodological advances in high-throughput analysis of the TCR repertoire, particularly at single-cell resolution. Recent studies have developed high-throughput sequencing technologies to identify TCR sequences and analyze their antigen specificities, providing important insights into T cell-mediated immune responses.
Article
Polymer Science
Nathan R. Richbourg, Nicholas A. Peppas
Summary: Accurate mathematical models linking solute and hydrogel properties to solute diffusion coefficients have been developed, with experiments showing that increasing hydrogel mesh radii can increase diffusivities of solutes, although the diffusivity behavior varies based on the solute size and type. The high-throughput characterization method for solute diffusion in hydrogels described in this study allows for precise design of hydrogels for biomedical applications.
Article
Chemistry, Analytical
Gerjen H. Tinnevelt, Kristiaan Wouters, Geert J. Postma, Rita Folcarelli, Jeroen J. Jansen
Summary: White blood cells play a crucial role in protecting the body from diseases, but can also be related to chronic inflammation, auto immune diseases, or leukemia. High-throughput analytical instruments have been developed to measure multiple proteins on millions of single cells to study the identity and function of different white blood cell types. Multivariate statistics are essential for fully extracting the information-rich biochemistry data. The process involves analyzing the study design, formulating a research question, preparing the data, converting single cells into a cellular distribution, and using clustering methods or models for analysis to differentiate cell (sub)types between groups.
ANALYTICA CHIMICA ACTA
(2021)
Article
Multidisciplinary Sciences
Neda Barghi, Claudia Ramirez-Lanzas
Summary: A method for accurately and rapidly measuring Drosophila egg size using large particle flow cytometry (LPFC) is presented in this study. The size estimates obtained using LPFC are precise and highly correlated with manual measurements. LPFC-based sorting of eggs does not affect their viability, making it a suitable approach for downstream analyses. The method can be applied to organisms within the detectable size range of large particle flow cytometers (10-1500 μm). The potential applications of this method and recommendations for optimization are discussed.
SCIENTIFIC REPORTS
(2023)
Review
Biotechnology & Applied Microbiology
Wen-min Zhou, Yan-yan Yan, Qiao-ru Guo, Hong Ji, Hui Wang, Tian-tian Xu, Bolat Makabel, Christian Pilarsky, Gen He, Xi-yong Yu, Jian-ye Zhang
Summary: The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, with traditional gene profiling methods often masking such differences. Single cell sequencing and microfluidic technologies have emerged as frontiers in research, providing insights into individual cell differences and complex cell populations. The advantages and disadvantages of common high-throughput single cell sequencing technologies are discussed, along with brief illustrations of microfluidic applications in single cell sequencing for cancer and immune system disease diagnosis.
JOURNAL OF NANOBIOTECHNOLOGY
(2021)
Article
Biochemical Research Methods
Shujie Yang, Joseph Rufo, Ruoyu Zhong, Joseph Rich, Zeyu Wang, Luke P. P. Lee, Tony Jun Huang
Summary: Acoustic tweezers offer an efficient and contact-free way to manipulate individual cells and particles. Its application in next-generation cellular assays can enhance our understanding of biological systems. With acoustic tweezers, users can conduct a variety of experiments involving trapping, patterning, pairing, and separating single cells in the field of biological sciences.
Article
Multidisciplinary Sciences
Taiken Nakashima, Haruka Tomobe, Takumi Morigaki, Mengfan Yang, Hiroto Yamaguchi, Yoichiro Kato, Wei Guo, Vikas Sharma, Harusato Kimura, Hitoshi Morikawa
Summary: Maize, the most produced cereal crop in the world, can prevent lodging by selecting cultivars with a high stem elastic modulus. In this study, an ultra-compact sensor array inspired by earthquake engineering was developed to evaluate the elastic modulus of maize cultivars efficiently. The estimated Young's modulus based on natural vibration analysis using finite element analysis (FEA) was representative of the individual Young's modulus. Furthermore, the FEA results identified the hotspots where the stalks were most deformed when subjected to wind vibration, allowing for the division of cultivars into phenotypic groups based on the position and number of hotspots.
SCIENTIFIC REPORTS
(2023)
Article
Chemistry, Multidisciplinary
Zhenyu Zou, Jialun Liang, Qian Jia, Di Bai, Wei Xie, Wenqiang Wu, Chuang Tan, Jie Ma
Summary: “The flow-cell system, developed in this work, combines gas-pumped calibration and fluorescence imaging to enable simultaneous single-molecule force measurement and visualization. It offers high force stability and tuning accuracy, allowing for precise force application and measurement. The system also facilitates real-time tracking of protein motors and monitoring of biomolecular conformational changes under controlled forces. The findings from this work lay down a valuable foundation for the flow-cell to be utilized as a versatile, quantitative, and high-throughput tool for single-molecule manipulation and visualization.”
Article
Pathology
Lars Egevad, Brett Delahunt, Hemamali Samaratunga, Toyonori Tsuzuki, Henrik Olsson, Peter Strom, Cecilia Lindskog, Tomi Hakkinen, Kimmo Kartasalo, Martin Eklund, Pekka Ruusuvuori
Summary: This study found significant variability in the assessment of perineural invasion in prostate biopsies, with good interobserver agreement but still room for improvement and standardization. Immunohistochemistry staining can help clarify the diagnosis of PNI.
Article
Biochemistry & Molecular Biology
Kaisa Liimatainen, Riku Huttunen, Leena Latonen, Pekka Ruusuvuori
Summary: This study explored the use of deep neural networks for classifying protein localizations in 13 cellular subcompartments. The results showed that the fully convolutional network outperformed the convolutional neural network in classifying images with multiple simultaneous protein localizations, demonstrating its potential for systematic protein localization assessment.
Article
Computer Science, Information Systems
Mira Valkonen, Gunilla Hognas, G. Steven Bova, Pekka Ruusuvuori
Summary: The study demonstrates the impact of histopathological sample fixation on the accuracy of a deep learning based nuclei detection model, highlighting the importance of considering the variability introduced during sample preparation when building accurate and robust algorithms. The dataset with over 67,000 annotated nuclei locations from 16 patients and three different sample fixation types provides a solid foundation for developing accurate and robust nuclei detection models and enables generalization to images from unseen domains through unsupervised domain adaptation.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen, Peter Strom, Hans Pinckaers, Kunal Nagpal, Yuannan Cai, David F. Steiner, Hester van Boven, Robert Vink, Christina Hulsbergen-van de Kaa, Jeroen van der Laak, Mahul B. Amin, Andrew J. Evans, Theodorus van der Kwast, Robert Allan, Peter A. Humphrey, Henrik Gronberg, Hemamali Samaratunga, Brett Delahunt, Toyonori Tsuzuki, Tomi Hakkinen, Lars Egevad, Maggie Demkin, Sohier Dane, Fraser Tan, Masi Valkonen, Greg S. Corrado, Lily Peng, Craig H. Mermel, Pekka Ruusuvuori, Geert Litjens, Martin Eklund
Summary: The PANDA challenge is the largest histopathology competition to date, aiming to catalyze the development of reproducible AI algorithms for Gleason grading in prostate cancer. The submitted algorithms achieved pathologist-level performance and their diversity and generalization were validated through cross-continental cohorts.
Article
Multidisciplinary Sciences
Pekka Ruusuvuori, Masi Valkonen, Kimmo Kartasalo, Mira Valkonen, Tapio Visakorpi, Matti Nykter, Leena Latonen
Summary: This article introduces a computational method for visualizing and quantitatively assessing histopathological alterations in three dimensions. By reconstructing the 3D representation of the whole organ and analyzing histological characteristics and regions of interest, tumors can be quantitatively evaluated and characterized. The 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology.
Article
Pathology
Kimmo Kartasalo, Peter Strom, Pekka Ruusuvuori, Hemamali Samaratunga, Brett Delahunt, Toyonori Tsuzuki, Martin Eklund, Lars Egevad
Summary: This study developed an artificial intelligence algorithm based on deep neural networks for detecting perineural invasion (PNI) in prostate biopsies. By digitizing and annotating a large number of biopsy samples, the algorithm achieved high accuracy in PNI detection, potentially providing valuable assistance to pathologists.
Article
Oncology
Mauro Scaravilli, Sonja Koivukoski, Andrew Gillen, Aya Bouazza, Pekka Ruusuvuori, Tapio Visakorpi, Leena Latonen
Summary: The study demonstrates that miR-32 promotes MYC-induced prostate adenocarcinoma and identifies PDK4 as a PC-relevant metabolic target of miR-32-3p. Elevated expression of miR-32 influences prostate tumor growth and phenotype, and regulates the expression of prostate secretome and several PC-related target genes.
Article
Chemistry, Multidisciplinary
Fabi Prezja, Ilkka Polonen, Sami Ayramo, Pekka Ruusuvuori, Teijo Kuopio
Summary: In this study, the staining variance between different laboratories on three tissue types was evaluated using machine learning. The results showed that both laboratory effects and tissue type bias contributed to the inter-laboratory staining variance.
APPLIED SCIENCES-BASEL
(2022)
Article
Pathology
Laura Mairinoja, Hanna Heikela, Sami Blom, Darshan Kumar, Anna Knuuttila, Sonja Boyd, Nelli Sjoblom, Eva-Maria Birkman, Petteri Rinne, Pekka Ruusuvuori, Leena Strauss, Matti Poutanen
Summary: The incidence of nonalcoholic fatty liver disease is increasing worldwide, and there is a need for new methods to study its manifestation and analyze drug efficacy in preclinical models. This study developed a deep neural network-based model to quantify different types of steatosis in the liver on stained whole slide images. The algorithm accurately detected liver parenchyma, excluded blood vessels and artifacts, differentiated microvesicular and macrovesicular steatosis, and quantified the recognized tissue area. The results correlated well with expert pathologists' evaluation and liver fat content measurements, demonstrating the reliability of the model.
AMERICAN JOURNAL OF PATHOLOGY
(2023)
Article
Multidisciplinary Sciences
Liisa Petainen, Juha P. Vayrynen, Pekka Ruusuvuori, Ilkka Polonen, Sami Ayramo, Teijo Kuopio
Summary: This study proposes an automated method for estimating tumor-stroma ratio (TSR) from histopathological images of colorectal cancer using convolutional neural networks. The models achieved high accuracy in classifying stroma, tumor, and other tissues, with the best model reaching 99.3% accuracy for tumor classification. The predicted TSR values showed moderate correlation with pathologist estimations (0.57).
Article
Computer Science, Artificial Intelligence
Umair Khan, Sonja Koivukoski, Mira Valkonen, Leena Latonen, Pekka Ruusuvuori
Summary: Conventional histopathology relies on chemical staining, which is a labor-intensive procedure that alters tissue irreversibly. Virtual staining using deep learning can overcome these drawbacks. In this study, we used unstained tissue sections and investigated the impact of increased network capacity on virtually stained images. By optimizing the network architecture, we achieved higher structural similarity, signal-to-noise ratio, and nuclei reproduction accuracy. Our findings demonstrate the potential of virtual staining in streamlining histopathological analysis.
Article
Computer Science, Artificial Intelligence
Joel Honkamaa, Umair Khan, Sonja Koivukoski, Mira Valkonen, Leena Latonen, Pekka Ruusuvuori, Pekka Marttinen
Summary: This work proposes a generic solution for cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method involves joint training of an image synthesis network and separate registration networks, allowing adversarial training even with misaligned data. It lowers the barriers for training cross-modality image synthesis networks for more difficult data sets.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Endocrinology & Metabolism
Oliver Meikar, Daniel Majoral, Olli Heikkinen, Eero Valkama, Sini Leskinen, Ana Rebane, Pekka Ruusuvuori, Jorma Toppari, Juho-Antti Makela, Noora Kotaja
Summary: Spermatogenesis is a complex differentiation process that occurs in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium allows for synchronous progression of germ cells at certain stages of differentiation. To facilitate the analysis of spermatogenesis, researchers have developed a convolutional deep neural network-based approach called STAGETOOL. STAGETOOL accurately classifies histological images of DAPI-stained mouse testis cross-sections into different stage classes and cell categories. It has high classification accuracy and can be applied for analyzing spermatogenic defects in knockout mouse models and profiling protein expression patterns. STAGETOOL represents a major advancement in male reproductive biology research.
Meeting Abstract
Urology & Nephrology
N. Mulliqi, K. Kartasalo, X. Ji, K. Szolnoky, H. Olsson, A. Blilie, M. Braun, M. Gambacorta, K. Hotakainen, E. A. M. Janssen, S. R. Kjosavik, R. Lowicki, B. G. Pedersen, K. D. Sorensen, B. P. Ulhoi, P. Ruusuvuori, L. Egevad, M. Eklund
Meeting Abstract
Urology & Nephrology
K. Kartasalo, W. Bulten, P-H. C. Chen, P. Strom, H. Pinckaers, K. Nagpal, P. Ruusuvuori, G. Litjens, M. Eklund