Ultrasensitive proteomic quantitation of cellular signaling by digitized nanoparticle-protein counting

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

Determining the Efficiency of Single Molecule Quantum Dot Labeling of HER2 in Breast Cancer Cells

Quantum dots exhibit unique properties compared to other fluorophores, such as bright fluorescence and lack of photobleaching, resulting in their widespread utilization as fluorescent protein labels in the life sciences. However, their application is restricted to relative quantifications due to lacking knowledge about the labeling efficiency. We here present a strategy for determining the labeling efficiency of quantum dot labeling of HER2 in overexpressing breast cancer cells. Correlative light- and liquid-phase electron microscopy of whole cells was used to convert fluorescence intensities into the underlying molecular densities of the quantum dots. The labeling procedure with small affinity proteins was optimized yielding a maximal labeling efficiency of 83%, which was applicable to the high amount of ∼1.5 × 10⁶ HER2 per cell. With the labeling efficiency known, it is now possible to derive the absolute protein expression levels in the plasma membrane and its variation within a cell and between cells.

Proteomics for early detection of colorectal cancer: recent updates

INTRODUCTION

Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.

Atomic psychiatry? An essay

OBJECTIVE

This article aims to address the need to rethink the classification of mental illness and to draw attention to the current use of quantum biology and its likely future use in understanding the nature of mental illness.

CONCLUSION

It is desirable to separate out neuroscience and clinical research and to become better acquainted with the concepts of quantum mechanical/quantum biological theory and its contribution to medicine.