Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

This work was supported by the National Institute of General Medical Sciences [GM131919].In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.PostprintPeer reviewe

Quantifying Golgi structure using EM : combining volume-SEM and stereology for higher throughput

John Lucocq was supported by a Programme grant from the Wellcome Trust (Number 045404). Sophie Ferguson was a recipient of a 600th anniversary studentship from the University of St Andrews.Investigating organelles such as the Golgi complex depends increasingly on high-throughput quantitative morphological analyses from multiple experimental or genetic conditions. Light microscopy (LM) has been an effective tool for screening but fails to reveal fine details of Golgi structures such as vesicles, tubules and cisternae. Electron microscopy (EM) has sufficient resolution but traditional transmission EM (TEM) methods are slow and inefficient. Newer volume scanning EM (volume-SEM) methods now have the potential to speed up 3D analysis by automated sectioning and imaging. However, they produce large arrays of sections and/or images, which require labour-intensive 3D reconstruction for quantitation on limited cell numbers. Here, we show that the information storage, digital waste and workload involved in using volume-SEM can be reduced substantially using sampling-based stereology. Using the Golgi as an example, we describe how Golgi populations can be sensed quantitatively using single random slices and how accurate quantitative structural data on Golgi organelles of individual cells can be obtained using only 5–10 sections/images taken from a volume-SEM series (thereby sensing population parameters and cell–cell variability). The approach will be useful in techniques such as correlative LM and EM (CLEM) where small samples of cells are treated and where there may be variable responses. For Golgi study, we outline a series of stereological estimators that are suited to these analyses and suggest workflows, which have the potential to enhance the speed and relevance of data acquisition in volume-SEM.Publisher PDFPeer reviewe

Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)

Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipoprotein(a) (Lp(a), or remnant lipoproteins (RLP) using electron microscopy combined with machine learning tools from microliter samples of human plasma. In the reported method, lipoproteins were absorbed onto electron microscopy (EM) support films from diluted plasma and embedded in thin films of methyl cellulose (MC) containing mixed metal stains, providing intense edge contrast. The results show that LPs have a continuous frequency distribution of sizes, extending from LDL (> 15 nm) to intermediate density lipoprotein (IDL) and very low-density lipoproteins (VLDL). Furthermore, mixed metal staining produces striking “positive” contrast of specific antibodies attached to lipoproteins providing quantitative data on apolipoprotein(a)-positive Lp(a) or apolipoprotein B (ApoB)-positive particles. To enable automatic particle characterization, we also demonstrated efficient segmentation of lipoprotein particles using deep learning software characterized by a Mask Region-based Convolutional Neural Networks (R-CNN) architecture with transfer learning. In future, EM and machine learning could be combined with microarray deposition and automated imaging for higher throughput quantitation of lipoproteins associated with CVD risk.Publisher PDFPeer reviewe

CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM

This work was supported by EMBL funds and by by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project number 240245660 – SFB 1129 (project Z2).In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume ultrastructural imaging. We present a toolset for adherent cells that enables tracking and finding cells, previously identified in light microscopy (LM), in the FIB-SEM, along with the automatic acquisition of high-resolution volume datasets. We detect the underlying grid pattern in both modalities (LM and EM), to identify common reference points. A combination of computer vision techniques enables complete automation of the workflow. This includes setting the coincidence point of both ion and electron beams, automated evaluation of the image quality and constantly tracking the sample position with the microscope’s field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 µm accuracy while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing volume acquisition in multiple locations autonomously is possible in EM.Publisher PDFPeer reviewe

Developments in cell biology for quantitative immunoelectron microscopy based on thin sections: a review

Quantitative immunoelectron microscopy uses ultrathin sections and gold particle labelling to determine distributions of molecules across cell compartments. Here, we review a portfolio of new methods for comparing labelling distributions between different compartments in one study group (method 1) and between the same compartments in two or more groups (method 2). Specimen samples are selected unbiasedly and then observed and expected distributions of gold particles are estimated and compared by appropriate statistical procedures. The methods can be used to analyse gold label distributed between volume-occupying (organelle) and surface-occupying (membrane) compartments, but in method 1, membranes must be treated as organelles. With method 1, gold counts are combined with stereological estimators of compartment size to determine labelling density (LD). For volume-occupiers, LD can be expressed simply as golds per test point and, for surface-occupiers, as golds per test line intersection. Expected distributions are generated by randomly assigning gold particles to compartments and expressing observed/expected counts as a relative labelling index (RLI). Preferentially-labelled compartments are identified from their RLI values and by Chi-squared analysis of observed and expected distributions. For method 2, the raw gold particle counts distributed between compartments are simply compared across groups by contingency table and Chi-squared analysis. This identifies the main compartments responsible for the differences between group distributions. Finally, we discuss labelling efficiency (the number of gold particles per target molecule) and describe how it can be estimated for volume- or surface-occupiers by combining stereological data with biochemical determinations

Altered ceramide metabolism is a feature in the extracellular vesicle-mediated spread of alpha-synuclein in Lewy body disorders

This study was funded by the Lewy Body Society and the Michael J. Fox Foundation for Parkinson’s Research. A.H. and B.M.B. gratefully acknowledge funding from the Knut och Alice Wallenberg Stiftelse through the Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden. S.T., M.K.D. and P.D.W. gratefully acknowledge the financial support of the European Regional Development Fund, Scottish Funding Council and Highlands and Islands Enterprise. Newcastle University TEM Services acknowledge BBSRC support (Grant code BB/R013942/1). The Research was supported by the National Institute for Health Research Newcastle Biomedical Research Centre based at Newcastle Hospitals NHS Foundation Trust and Newcastle University. The Newcastle Brain Tissue Resource is supported by grants from the UK Medical Research Council and the Brains for Dementia Research, a joint venture between Alzheimer’s Society and Alzheimer’s Research UK.Mutations in glucocerebrosidase (GBA) are the most prevalent genetic risk factor for Lewy body disorders (LBD)—collectively Parkinson’s disease, Parkinson’s disease dementia and dementia with Lewy bodies. Despite this genetic association, it remains unclear how GBA mutations increase susceptibility to develop LBD. We investigated relationships between LBD-specific glucocerebrosidase deficits, GBA-related pathways, and α-synuclein levels in brain tissue from LBD and controls, with and without GBA mutations. We show that LBD is characterised by altered sphingolipid metabolism with prominent elevation of ceramide species, regardless of GBA mutations. Since extracellular vesicles (EV) could be involved in LBD pathogenesis by spreading disease-linked lipids and proteins, we investigated EV derived from post-mortem cerebrospinal fluid (CSF) and brain tissue from GBA mutation carriers and non-carriers. EV purified from LBD CSF and frontal cortex were heavily loaded with ceramides and neurodegeneration-linked proteins including alpha-synuclein and tau. Our in vitro studies demonstrate that LBD EV constitute a “pathological package” capable of inducing aggregation of wild-type alpha-synuclein, mediated through a combination of alpha-synuclein–ceramide interaction and the presence of pathological forms of alpha-synuclein. Together, our findings indicate that abnormalities in ceramide metabolism are a feature of LBD, constituting a promising source of biomarkers, and that GBA mutations likely accelerate the pathological process occurring in sporadic LBD through endolysosomal deficiency.Publisher PDFPeer reviewe

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose: Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods: Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results: The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion: We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

Analysis of specificity in immunoelectron microscopy

Immunoelectron microscopy (immuno-EM) using gold labeling on sections is a powerful technique for mapping the distribution of proteins, lipids, carbohydrates, and nucleic acids in intact biological systems. The gold particles comprise a useful and readily quantifiable digital readout. Simply applying a labeling reagent (antibody or other affinity probe) to an ultrathin section yields a pattern of gold signal over the biological structures displayed in the section. This initial (raw) distribution of gold signal contains both specific and nonspecific labeling. Here we describe a method for removing nonspecific labeling to leave the target-specific signal. This specific labeling distribution better reflects the "real" distribution of the cell component of interest

Analysis of specificity in immunoelectron microscopy

Immunoelectron microscopy (immuno-EM) using gold labeling on sections is a powerful technique for mapping the distribution of proteins, lipids, carbohydrates, and nucleic acids in intact biological systems. The gold particles comprise a useful and readily quantifiable digital readout. Simply applying a labeling reagent (antibody or other affinity probe) to an ultrathin section yields a pattern of gold signal over the biological structures displayed in the section. This initial (raw) distribution of gold signal contains both specific and nonspecific labeling. Here we describe a method for removing nonspecific labeling to leave the target-specific signal. This specific labeling distribution better reflects the "real" distribution of the cell component of interest

Quantifying immunogold labelling patterns of cellular compartments when they comprise mixtures of membranes (surface-occupying) and organelles (volume-occupying)

In quantitative immunoelectron microscopy, subcellular compartments that are preferentially labelled with colloidal gold particles can be identified by estimating labelling densities (LDs) and relative labelling indices (RLIs). Hitherto, this approach has been limited to compartments which are either surface occupying (membranes) or volume occupying (organelles) but not a mixture of both (membranes and organelles). However, some antigens are known to translocate between membrane and organelle compartments and the problem then arises of expressing gold particle LDs in a consistent manner (e. g., as number per compartment profile area). Here, we present one possible solution to tackle this problem. With this method, each membrane is treated as a volume-occupying compartment and this is achieved by creating an acceptance zone at a fixed distance on each side of membrane images. Gold signal intensity is then expressed as an LD within the membrane profile area so created and this LD can be compared to LDs found in volume-occupying compartments. Acceptance zone width is determined largely by the expected dispersion of gold labelling. In some cases, the zone can be applied to all visible membrane images but there is a potential problem when image loss occurs due to the fact that membranes are not cut orthogonal to their surface but are tilted within the section. The solution presented here is to select a subset of clear images representing orthogonally sectioned membranes (so-called local vertical windows, LVWs). The fraction of membrane images forming LVWs can be estimated in two ways: goniometrically (by determining the angle at which images become unclear) or stereologically (by counting intersections with test lines). The fraction obtained by either method can then be used to calculate a factor correcting for membrane image loss. In turn, this factor is used to estimate the total gold labelling associated with the acceptance zone of the entire (volume-occupying) membrane. However calculated, the LDs over the chosen (membrane and organelle) compartments are used to obtain observed and expected gold particle counts. The observed distribution is determined simply by counting gold particles associated with each compartment. Next, an expected distribution is created by randomly superimposing test points and counting those hitting each compartment. LDs of the chosen compartments are used to calculate RLI and chi-squared values and these serve to identify those compartments in which there is preferential labelling. The methods are illustrated by synthetic and real data.</p