Authenticating Hybrid Cell Lines

Hybrid (both intra-species and inter-species) cell lines arise through intentional or nonintentional fusion of somatic cells having different origins. Hybrid cell lines can pose a problem for authentication testing to confirm cell line identity, since the results obtained may not conform to the results expected for the two parental cell types. Thus, depending on the identity testing methodology, a hybrid cell may display characteristics of one of the parental cell type or of both. In some instances, the hybrid cell line may display characteristics that are different from those displayed by either parental cell type; these differences may not necessarily indicate cellular cross-contamination. Testing should be performed as soon as possible after an intended fusion has occurred, so that a baseline reference profile is available for later comparison. In this article, we describe the various approaches that have been used for identifying hybrid cell lines and the results that might be expected when using various technologies for this purpose

Recommendation of short tandem repeat profiling for authenticating human cell lines, stem cells, and tissues

Cell misidentification and cross-contamination have plagued biomedical research for as long as cells have been employed as research tools. Examples of misidentified cell lines continue to surface to this day. Efforts to eradicate the problem by raising awareness of the issue and by asking scientists voluntarily to take appropriate actions have not been successful. Unambiguous cell authentication is an essential step in the scientific process and should be an inherent consideration during peer review of papers submitted for publication or during review of grants submitted for funding. In order to facilitate proper identity testing, accurate, reliable, inexpensive, and standardized methods for authentication of cells and cell lines must be made available. To this end, an international team of scientists is, at this time, preparing a consensus standard on the authentication of human cells using short tandem repeat (STR) profiling. This standard, which will be submitted for review and approval as an American National Standard by the American National Standards Institute, will provide investigators guidance on the use of STR profiling for authenticating human cell lines. Such guidance will include methodological detail on the preparation of the DNA sample, the appropriate numbers and types of loci to be evaluated, and the interpretation and quality control of the results. Associated with the standard itself will be the establishment and maintenance of a public STR profile database under the auspices of the National Center for Biotechnology Information. The consensus standard is anticipated to be adopted by granting agencies and scientific journals as appropriate methodology for authenticating human cell lines, stem cells, and tissues

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package

Identifying Hazards at Work

<h2><i>Identifying Hazards at Work</i></h2> <p>1. How are hazards identified?</p> <p> a. Inspection of the Workplace</p> <p> b. Consultation on health and safety issues</p> <p> c. Review of available information e.g. safety data</p> <p><br></p><p>2. Commonly occurring hazards</p> <p> a. Give examples of general hazards that may occur anywhere</p> <p> b. Give examples of specific hazards that are present in your workplace</p> <p><br></p><p>3. Practical examples</p> <p> a. Identify hazards in a series of photographs</p> <p> b. For each hazard, list the types of harm that may occur</p

Assessing Risks

<p><i><b>Assessing Risks at Work</b></i></p><p> <b><br></b></p><p>1. When should a Risk Assessment be carried out?</p><p>a. A work activity is new or when it changes</p><p>b. The hazards associated with a work activity are not clear</p><p>c. There are multiple hazards and it is unclear how those hazards may interact and what the overall risk may be</p><p>d. Mandatory for some hazards (Safe Work Methods Statement (SWMS))</p><p> <br></p><p>2. How should risk be assessed?</p><p>a. Consider how hazard may cause harm</p><p>b. Assess the likelihood of harm occurring</p><p>c. Assess how severe the harm could be</p><p> <br></p><p>3. Practical examples</p><p>a. Assess risks associated with hazards in a series of photographs</p><p>b. Use a risk matrix</p><p>c. Write up a risk assessment<br></p

Managing Risks

<h2><i>Managing Risks at Work</i></h2> <p>1. What are the aims of risk control/management?</p> <p>a. Eliminate the risk ‘so far as is reasonably practicable’</p> <p>b. If this is not possible, minimise the risk ‘so far as is reasonably practicable’</p> <p>2. List the three levels in the risk control hierarchy</p> <p>a. Level 1 Risk Control: Eliminate the hazards</p> <p>b. Level 2 Risk Control: Substitute the hazard with something safer, isolate the hazard from people, use engineering controls</p> <p>c. Level 3 Risk Control: Use administrative controls, personal protective equipment (PPE)</p> <p>3. Give examples of risk controls in each category</p> <p>a. Level 1 </p> <p>b. Level 2 </p> <p>c. Level 3 </p

Introduction to Work Health and Safety

<h2><i>Introduction to Work Health and Safety (WHS)</i></h2> <p>1. Why is WHS important? </p> <p> a. Give an example of why safety is important </p> <p> b. Why is safety like carrying a baton?</p> <p> c. Name the relevant legislation (Work Health and Safety Act/Regulation)</p> <p><br></p><p>2. Definitions</p> <p> a. Hazard</p> <p> b. Risk</p> <p> c. Risk control</p> <p><br></p><p>3. Who is responsible for WHS?</p> <p> a. Define Persons Conducting a Business or Undertaking (PCBU)</p> <p> b. List various types of Workers</p> <p> c. Give an example of a consultation mechanism</p

Is Cell Culture a Risky Business?

This seminar asks the question "Is Cell Culture a Risky Business?" and discusses the various risks that may apply. Risks are divided into:<div><br></div><div>1. Risks to the operator - safety</div><div>2. Risks to the donor - ethics</div><div>3. Risks to experimental work - validation testing</div><div>4. Risks to sample integrity - cell banking, storage and transport</div><div>5. Risks to publications - reproducibility.</div><div><br></div><div>The seminar was prepared for an Australian audience but most slides are relevant to international laboratories.</div><div><br></div

What are Misidentified Cell Lines?

<p>The International Cell Line Authentication Committee (ICLAC) raises awareness about misidentified cell lines and cross-contamination - common problems affecting cell culture.  Here we look at what misidentified cell lines actually are.</p

CLASTR : the cellosaurus STR similarity search tool ‐ A precious help for cell line authentication

Despite an increased awareness of the problematic of cell line cross-contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL-3.0 license