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A Novel Automated Mass Digitisation Workflow for Natural History Microscope Slides.

ABSTRACT: The Natural History Museum, London (NHM) has embarked on an ambitious programme to digitise its collections. One aim of the programme has been to improve the workflows and infrastructure needed to support high-throughput digitisation and create comprehensive digital inventories of large scientific collections. This paper presents the workflow developed to digitise the entire Phthiraptera (parasitic lice) microscope slide collection (70,663 slides). Here we describe a novel process of semi-automated mass digitisation using both temporary and permanent barcode labels applied before and during slide imaging. By using a series of barcodes encoding information associated with each slide (i.e. unique identifier, location in the collection and taxonomic name), we can run a series of automated processes, including file renaming, image processing and bulk import into the NHM's collection management system. We provide data on the comparative efficiency of these processes, illustrating how simple activities, like automated file renaming, reduces image post-processing time, minimises human error and can be applied across multiple collection types.

SUBMITTER: Allan EL

PROVIDER: S-EPMC6408422 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Novel Automated Mass Digitisation Workflow for Natural History Microscope Slides.

Allan E Louise EL Livermore Laurence L Price Benjamin W BW Shchedrina Olha O Smith Vincent S VS

Biodiversity data journal 20190301

The Natural History Museum, London (NHM) has embarked on an ambitious programme to digitise its collections. One aim of the programme has been to improve the workflows and infrastructure needed to support high-throughput digitisation and create comprehensive digital inventories of large scientific collections. This paper presents the workflow developed to digitise the entire Phthiraptera (parasitic lice) microscope slide collection (70,663 slides). Here we describe a novel process of semi-automa ...[more]

PMID: 30863197

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Project description:Digitisation of natural history collections has evolved from creating databases for the recording of specimens' catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

Project description:PurposeTo develop a more efficient impression cytology (IC) method for the transfer of ocular surface cells onto glass microscope slides for cytochemical, immunocytochemical, and immunofluorescence studies.MethodsCells are lifted off the ocular surface with a mixed cellulose ester membrane and then firmly attached to a glass slide using a novel triblock copolymer comprised of collagen type I, polyethylenimine and poly-L-lysine (CPP), and crosslinking cells and glass slide by heating and cooling. The membrane is removed intact after softening it with a butanol/ethanol solution. Transfer of cells is complete in about 10-15 minutes and is ready for staining. The efficiency of our cell transfer method was compared to current methods based on poly-L-lysine and albumin paste.ResultsOur method ensured almost complete transfer of cells. In contrast, the transfer of rabbit conjunctiva cells onto poly-L-lysine-covered slides was 37.5 ± 6.3% lower, and onto albumin-paste covered slides 62.5 ± 5.6% lower (mean ± SD); the transfer of rabbit goblet cells was even less efficient. The new method was also more efficient for transfer of cells from human oral mucosa obtained by IC. Transferred cells were successfully stained with H&E, chemiluminescence, and immunofluorescence agents. Using our method, we stained ocular surface cells for S100A4 and ATF4, both of which play a role in the pathophysiology of dry eye disease. We obtained similar results with oral mucosal cells, suggesting the generalizability of our approach. We propose an explanation for the strong adhesion of cells to the glass slide, which is based on their interactions with the triblock copolymer.ConclusionsWe developed a novel approach for the efficient and rapid transfer of cells obtained by IC onto glass microscope slides using a novel copolymer. Compared to available methods, our improved approach makes IC robust and simple, and should increase its diagnostic yield and clinical applicability.

Dataset Information

A Novel Automated Mass Digitisation Workflow for Natural History Microscope Slides.

Publications

A Novel Automated Mass Digitisation Workflow for Natural History Microscope Slides.

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