Object orientated automated image analysis: quantitative and qualitative estimation of inflammation in mouse lung

Historically, histopathology evaluation is performed by a pathologist generating a qualitative assessment on thin tissue sections on glass slides. In the past decade, there has been a growing interest for tools able to reduce human subjectivity and improve workload. Whole slide scanning technology combined with object orientated image analysis can offer the capacity of generating fast and reliable results. In the present study, we combined the use of these emerging technologies to characterise a mouse model for chronic asthma. We monitored the inflammatory changes over five weeks by measuring the number of neutrophils and eosinophils present in the tissue, as well as, the bronchiolar associated lymphoid tissue (BALT) area on whole lungs sections. We showed that inflammation assessment could be automated efficiently and reliably. In comparison to human evaluation performed on the same set of sections, computer generated data was more descriptive and fully quantitative. Moreover optimisation of our detection parameters allowed us to be to more sensitive and to generate data in a larger dynamic range to traditional experimental evaluation, such as bronchiolar lavage (BAL) inflammatory cell counts obtained by flow cytometry. We also took advantage of the fact that we could increase the number of samples to be analysed within a day. Such optimisation allowed us to determine the best study design and experimental conditions in order to increase statistical significance between groups. In conclusion, we showed that combination of whole slide digital scanning and image analysis could be fully automated and deliver more descriptive and biologically relevant data over traditional methods evaluating histopathological pulmonary changes observed in this mouse model of chronic asthma

The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation.

OBJECTIVES: The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment. METHODS: The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms. RESULTS: Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed. CONCLUSIONS: mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force

Chemical reagents, their purity and tests, authorized translation of Prüfung der chemischen reagenzien auf reinheit (2. aufl.)

Mode of access: Internet

Chemical reagents, their purity and tests; a new and improved text based on and replacing the latest edition of Krauch's "Die prüfung der chemischen reagentien auf reinheit,"

Mode of access: Internet

Dexfenfluramine discontinuous treatment does not worsen hypoxia-induced pulmonary vascular remodeling but activates RhoA/ROCK pathway: Consequences on pulmonary hypertension

International audienc

Comparison between manual and computerised assessment of pulmonary inflammation in mouse receiving House Dust Mite (HDM) extracts over 5 weeks

A. Manual assessment of pulmonary inflammation in mouse performed by a pathologist. B. Automated assessment of pulmonary inflammation done by combining 3 signs of inflammation: total number of neutrophils detected, total number of eosinophils detected and total area covered by inflammatory cells.<p><b>Copyright information:</b></p><p>Taken from "Object orientated automated image analysis: quantitative and qualitative estimation of inflammation in mouse lung"</p><p>http://www.diagnosticpathology.org/content/3/S1/S16</p><p>Diagnostic Pathology 2008;3(Suppl 1):S16-S16.</p><p>Published online 15 Jul 2008</p><p>PMCID:PMC2500097.</p><p></p

Influence of the normalisation factor on mucin secretion detection levels

A, B and C Panels show classification views of a mouse tissue section after analysis. The red surface represents the area covered by mucin (our nominator), and the light green surface represents the tissue area used for normalisation purposes (our denominator). We used at first the whole tissue section (excluding the air space) as shown in panel A, which generated the results shown in the graph D. We then excluded alveolar tissue as shown in panel B, and generated the results shown in the graph E. At last we excluded inflammatory infiltrate as shown in panel C, and generated the results shown in the graph F.<p><b>Copyright information:</b></p><p>Taken from "Object orientated automated image analysis: quantitative and qualitative estimation of inflammation in mouse lung"</p><p>http://www.diagnosticpathology.org/content/3/S1/S16</p><p>Diagnostic Pathology 2008;3(Suppl 1):S16-S16.</p><p>Published online 15 Jul 2008</p><p>PMCID:PMC2500097.</p><p></p