Penicillium poederi and P. tirolense, two new species of section Torulomyces

Here we describe two new species of the genus Penicillium section Torulomyces with solitary phialides. Penicillium poederi sp. nov. was isolated from volcanic soils in Iceland. Penicillium tirolense sp. nov. was isolated from a sporocarp of Serpula lacrymans. Both species are characterised by slow growth rates and the production of a brown soluble pigment on CYA, conidiophores with solitary ampulliform phialides with smooth-walled stipes and warty, globose conidia and with connectives without visible rings. The spores of. P. poederi are 2.5 μm diam, while the spores of P. tirolense are 2.0 μm diam. In a multigene phylogeny based on the ITS, BenA, CaM and RPB2 gene regions P. tubakianum and P. wollemiicola are the closest relatives of P. poederi. This species differs from P. tubakianum and P. wollemiicola by its growth rates and by its pigmentation. The holotype of P. poederi is IB2017/0007, while SF014017 (CBS 147622) is a culture derived from the holotype. The closest relatives of P. tirolense are P. austricola and P. riverlandense. It differs from P. austricola by lower growth rates on all tested media and temperatures and by its larger spores. It differs from P. riverlandense by lower growth rates and the absence of growth at 37 °C. The holotype of P. tirolense is IBF2019/0162, while SF015108 (CBS 147625) is a culture derived from the holotype. Citation: Kirchmair M, Embacher J, Heimdörfer D, Walch G, Neuhauser S (2022). Penicillium poederi and Penicillium tirolense, two new species of section Torulomyces. Fungal Systematics and Evolution 10: 91-101. doi: 10.3114/fuse.2022.10.03.

Abstract 1190: MMHCdb: A knowledgebase for the evolving landscape of mouse models of human cancer

The laboratory mouse is the premier mammalian model organism for interrogating the genetic and molecular basis of human cancer and for preclinical investigations into targets for the prevention and treatment of cancer. The distributed and heterogenous nature of information about these model systems makes it difficult for researchers to integrate and interpret the information to determine the state of the field and to identify the most relevant models for basic and preclinical research. The Mouse Models of Human Cancer database (http://tumor.informatics.jax.org) is an expertly curated knowledgebase about genetically defined mouse strains and Patient Derived Xenograft (PDX) models of human cancer. Data in MMHCdb are obtained from peer-reviewed scientific publications and direct data submissions from individual investigators and large-scale programs. MMHCdb is built on FAIR data management principles (Findable, Accessible, Interoperable, Reusable). The enforcement of metadata standards and official gene, allele and strain nomenclature ensure accurate and comprehensive search results for cancer models. MMHCdb has long represented data from spontaneous or endogenously induced tumors from genetically defined mice and for PDXs which have been the foundation of basic cancer research and preclinical studies for decades. MMHCdb has expanded to include cancer models such as Diversity Outbred and Collaborative Cross mice which are ideally suited for research into the relationship of genetic variation with cancer susceptibility and for modeling the genetics of variability in treatment responses. The MMHCdb contains over 109,266 curated tumor frequency records for over 8,275 mouse strains. Tumor types in the database have been indexed to over 21,000 literature citations. PDX models and data available in MMHCdb are also accessible from the Patient Derived Cancer Models resource at EMBL-EBI which currently provides information for over 4,000 PDXs (https://cancermodels.org).MMHCdb is supported by NCI R01 CA089713

Citation Format: Dale A. Begley, Debbie M. Krupke, Steven Neuhauser, John Sundberg, Carol J. Bult. MMHCdb: A knowledgebase for the evolving landscape of mouse models of human cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1190.

Abstract LB061: Evaluation of the in vivo efficacy of the B7-H3 targeting antibody-drug conjugate (ADC) DS7300a: A report fro the Pediatric Preclinical In Vivo Resting (PIVOT) program

Introduction: B7-H3 (encoded by the CD276 gene) is suggested to act as an immune checkpoint molecule and is highly expressed in some pediatric solid tumors. Monoclonal antibodies targeting B7-H3 (8H9 and MGA271) are well-tolerated but have demonstrated limited success in clinical trials. DS-7300a is a B7-H3 targeting ADC with a payload of DXd (an exatecan derivative that inhibits DNA topoisomerase I). DS-7300a has a drug-to-antibody ratio of 4 and it has shown promising early clinical activity in adults with advanced solid cancers (Johnson, Annals of Oncology 2021; 32:S583-S585).

Methods: Xenograft models for several pediatric cancer types were selected for preclinical testing of DS-7300a based on the high rates of B7-H3 expression in these cancers. Models were dosed at 10 mg/kg administered intravenously every other week for two doses (Q2wk x 2). Different experimental designs were used for efficacy testing. For rhabdomyosarcoma, Ewing sarcoma, Wilms tumor, and other pediatric solid tumors a single mouse trial (SMT) design with 40 models was used. A cohort design (N=10) was used for efficacy testing in 6 osteosarcoma models, 3 patient-derived orthotopic xenograft glioblastoma (PDOX) models, and 3 PDOX ependymoma models. For neuroblastoma models, 10 models (2 mice per model) were tested. To evaluate treatment efficacy, objective response measures based on changes in relative tumor volume were used (Houghton, Pediatr Blood Cancer 2007;49:928-940).

Results: As a single agent, DS-7300a induced a statistically significant prolongation of survival in 2 of 3 orthotopic glioblastoma and 1 of 3 ependymal tumor models. DS-7300a demonstrated high efficacy (maintained complete response, complete response, and partial response) in most non-CNS models tested: 17 of 21 rhabdomyosarcomas, 5 of 7 osteosarcomas, 7 of 10 neuroblastomas and 2 of 2 Wilms tumors. For Ewing sarcoma, most models were classified as progressive disease (9 of 15). For osteosarcoma models, the log cell kill per dose values ranged from 0.95 to 2.76 indicating high activity of the agent in these models. The activity for DS-7300a followed the general pattern of protein and RNA expression levels for B7-H3/CD276 in the models, with Ewing sarcoma models showing lower expression compared to the other models.

Conclusions: DS-7300a shows tumor-regressing anticancer activity across a wide range of pediatric solid tumor models. The maintained complete remissions observed for osteosarcoma models are noteworthy, as this level of response is uncommon for these models and as osteosarcoma shows the highest B7-H3 expression among pediatric cancers. The high level of preclinical activity observed for DS-7300a combined with the promising early clinical activity observed for adult patients provide strong rationale for studying DS-7300a in children with B7-H3 expressing solid tumors.

Citation Format: Richard Gorlick, E. Anders Kolb, Yifei Wang, Peter Houghton, Raushan Kurmasheva, Yael Mosse, John Maris, Matthew Tsang, David Groff, Kateryna Krytska, Xiao-Nan Li, Yuchen Du, Jun Hasegawa, Nanae Izumi, Steven Neuhauser, Anuj Srivastava, Tim Stearns, Vivek Philip, Emily L. Jocoy, Jeff Chuang, Carol J. Bult, Beverly Teicher, Malcolm Smith. Evaluation of the in vivo efficacy of the B7-H3 targeting antibody-drug conjugate (ADC) DS7300a: A report fro the Pediatric Preclinical In Vivo Resting (PIVOT) program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB061.

Abstract 3105: PDCM Finder: An open global cancer research platform for patient-derived cancer models

PDCM Finder (https://cancermodels.org) is a new portal that aggregates patient-derived models (xenografts, cell lines and organoids) from 27 academic and commercial providers, enables users to search and compare over 6000 models and associated molecular data, and connects users with model providers to facilitate collaboration among researchers.

Users can search for models of interest by either (1) exploring molecular data summaries for models of specific cancer types, or (2) using the intuitive search and faceted filtering options of the web user interface or (3) access resource database via REST API to run their own analysis. The data includes gene expression, gene mutation, copy number alteration, cytogenetics, patient treatment and drug dosing studies. We link external resources like publication platforms and cancer specific annotation tools enabling exploration and prioritization of PDCM variation data (COSMIC, CIViC, OncoMX, OpenCRAVAT).

In addition to exploring PDCM metadata and data, the portal enables users to validate their PDX models against PDX MI standard, get a FAIRness score of the model of interest, explore originating resource data processing protocols, training materials and contact the model supplier/provider.

PDCM Finder builds on the success of the PDX Finder resource (PMID:30535239). Critical PDCM attributes, such as diagnosis, drug names and genes, are harmonized and integrated into a cohesive ontological model based on the PDX Minimal information standard (PDX MI, PMID: 29092942). PDX MI has become established in the community for data exchange, adopted by the PDX providers, consortia and informatics tools integrating PDX data. We are working with the community on the PDCM Minimal Information standard in an effort to make PDCM datasets adhere to the FAIR data principles. We are driving the development of and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. We provide expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. PDCM Finder is freely available under an Apache 2.0 license (https://github.com/PDCMFinder). This work is supported by NCI U24 CA204781 01, U24 CA253539, and R01 CA089713. We welcome feedback on the resource and are looking for participants for usability studies - please get in touch if interested.

Citation Format: Zinaida Perova, Csaba Halmagyi, Alex Follette, Mauricio Martinez, Federico Lopez-Gomez, Jeremy Mason, Abayomi Mosaku, Nathalie Conte, Ross Thorne, Steven Neuhauser, Dale Begley, Debra Krupke, Terrence Meehan, Carol Bult, Helen Parkinson. PDCM Finder: An open global cancer research platform for patient-derived cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3105.

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

The EurOPDX Data Portal: an open platform for patient-derived cancer xenograft data sharing and visualization

Background

Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons – ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium.

Main body

In this paper we describe the main features of the EurOPDX Data Portal (https://dataportal.europdx.eu/), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views.

(Short) Conclusion

EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users’ interests.

Abstract 3017: Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal

We created the PDX Network (PDXNet) Portal to provide an intuitive way for researchers to explore and understand the models, sequencing data, and bioinformatics workflows generated by NCI's PDXNet consortium for research access (https://portal.pdxnetwork.org/). The portal also provides metrics for PDXNet's activities, data processing protocols, and training materials for processing PDX data.

The PDXNet Portal highlights model and data resources that include 216 new models across 29 cancer types. The most prevalent cancers represented in the PDX model dataset include invasive breast carcinoma (30.6%), melanoma (18.1%), and adenocarcinoma (14.4%). PDXNet teams have provided 2263 sequencing files from 356 samples across 204 patients, comprising whole exome (82.9%) and RNA seq files (17.1%). The most prevalent cancers represented in the PDXNet sequencing data set include Breast Pleural Effusion (27.2%), Breast Poorly Differentiated (12.5%), and Lung Adenocarcinoma (9.6%). The portal also provides access to 9492 sequencing files across 78 disease types that include 2594 samples across 463 patients uploaded from the NCI Patient-Derived Model Repository. The dataset includes both whole exomes (52.8%) and RNA seq (47.2%) data. The PDMR samples include PDX (82.7%), primary tumor (5.7%), normal germline (5.5), organoid culture (3.2), and Mixed Tumor Culture (2.9). The PDMR dataset also has multiple passages: P0 (21.8%), P1(39.5%), P2 (25.6%), and P3 (8.5%). These models and data resources support ten PDXNet Pilot activities, multiple publications, and international collaborations.

PDXNet has also developed a set of 13 robust, validated, and standardized workflows for processing PDXNet whole-exome and RNA seq data. Collectively, these workflows allow for the standardized processing of PDX and complementary human tissues (normal and tumor).

Our plan is to continuously update the model and data lists on the PDX portal as resources are generated. We expect that the PDXNet generated models, scheduled to grow to 1000 new models by 2022, will support multi-agent treatment studies, determination of mechanisms of sensitivity and resistance, and pre-clinical trials for example through the COMBO-MATCH program. The robust standard workflows currently processing all PDX sequencing data may also facilitate harmonizing data across studies. Lastly, we expect that the generated sequencing data will support computational approaches for studying cancer evolution and the mechanisms underlying cancer treatments.

Citation Format: Soner Koc, Mike Lloyd, Steven Neuhauser, Javad Noodbakhsh, Anuj Srivastava, Xing Yi Woo, Ryan Jeon, Jeffrey Grover, Sara Seepo, Christian Frech, Jack DiGiovanna, PDXNet Consortium, Yvonne A. Evard, Tiffany Wallace, Jeffrey Moscow, James H. Doroshow, Nicholas Mitsuade, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis Carvarjal-Carmona, Alana Welm, Bryan Welm, Michael T. Lewis, Govindan Ramaswamy, Li Ding, Shunquang Li, Meenherd Herlyn, Mike Davies, Jack Roth, Funda Meric-Bernstam, Peter Robinson, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3017.

Abstract 3212: PDX Finder: Largest global catalog of patient tumor derived xenograft models

Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDX repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world's largest open PDX database containing phenomic information of over 2900 models (www.pdxfinder.org, †). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines the minimal necessary metadata required to describe models (††). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive, faceted search interface allows users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data details pages where all available information can be browsed and downloaded. To further facilitate users' model selection, we link key external resources like publication platforms and cancer specific annotation tools, enabling exploration and prioritisation of PDX variation data (COSMIC, CivicDb, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration.

Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatic tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modelling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximising the investment in, and translational capabilities of these important models of human cancer.

PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01, R01 CA089713 and the European Molecular Biology Laboratory.

† Conte et al, 2019. PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery. NAR, 2019 Jan.†† Meehan, Conte et al, 2017. PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res. 2017 Nov.PDXNet: www.pdxnetwork.org, PDMR: pdmr.cancer.gov, EUROPDX: www.europdx.eu

Citation Format: Nathalie Conte, Csaba Halmagyi, Abayomi Mosaku, Jeremy C. Mason, Alex W. Follette, Ross Thorne, Steven Neuhauser, Dale Begley, Debbie M. Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: Largest global catalog of patient tumor derived xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3212.

PDX Finder: A portal for patient-derived tumor xenograft model discovery

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients’ tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the ‘findability’ of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

Abstract 2461: PDX Finder: A free and global catalog of patient tumor derived xenograft models

Patient-derived tumor xenograft (PDX) mouse models are an important oncology platform for cancer research, drug development and personalized medicine that are available from academic labs, large research consortia and contract research organizations (CROs). Because of the distributed and heterogeneous nature of repositories, finding models of interest is a challenge. To address this issue, The Jackson Laboratory and EMBL-EBI have co-developed the PDX Finder† (www.pdxfinder.org), a comprehensive, free and global catalogue of approximately 2000 PDX models and their associated data across repositories. To support the integration of data and make the discovery of relevant PDX models easier, we coordinated a community initiative to develop the PDX Minimal Information standard (PDX-MI). PDX-MI defines the metadata necessary for describing key elements of a PDX model including clinical attributes of a patient’s tumor, xenograft methods of implantation, host strain and model quality assurance methodology††. Using PDX-MI, model attributes are harmonized within PDX Finder into a cohesive ontological data model that supports integration from different resources and allows for comprehensive search and filtering options. PDX Finder also provides access to patient treatment information, genomic and drug dosing study data when available. Advanced search and filtering options allow researchers to find PDX models based on multiple attributes such as diagnosis (e.g. invasive ductal carcinoma), various tumor attributes (e.g. metastasis or primary), availability of specific datasets (e.g. mutation, dosing studies), markers (e.g. KRAS V600E) or results from drug resistance/sensitivity studies (e.g. resistance to cetuximab). From PDX Finder, direct links to originating resources are provided to allow users to contact the relevant institution for model acquisition and collaboration. PDX Finder is formally collaborating and providing critical software components to support several worldwide consortia including NCI’s PDXNet and EurOPDX. Individuals and organizations that generate or distribute PDX models are encouraged to register their models with PDX Finder. We also encourage researchers to explore the website to find PDX models and provide feedback as we continue to build this rich resource. Software components developed by the PDX Finder team are freely available under an Apache 2 license and source code is available at GitHub (github.com/pdxfinder). PDX Finder is supported by NCI U24 CA204781 01, R01 CA089713 and the European Molecular Biology Laboratory.

† Conte et al, 2019. PDX Finder: A Portal for Patient-Derived Tumor Xenograft Model Discovery. NAR, in press.

†† Meehan, Conte et al, 2017. PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res. 2017 Nov.

Citation Format: Nathalie Conte, Jeremy C. Mason, Csaba Halmagyi, Abayomi Mosaku, Steven Neuhauser, Dale A. Begley, Debra M. Krupke, Helen Parkinson, Terrence F. Meehan, Carol J. Bult. PDX Finder: A free and global catalog of patient tumor derived xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2461.

Cross-organism analysis using InterMine

InterMine is a data integration warehouse and analysis software system developed for large and complex biological data sets. Designed for integrative analysis, it can be accessed through a user-friendly web interface. For bioinformaticians, extensive web services as well as programming interfaces for most common scripting languages support access to all features. The web interface includes a useful identifier look-up system, and both simple and sophisticated search options. Interactive results tables enable exploration, and data can be filtered, summarized, and browsed. A set of graphical analysis tools provide a rich environment for data exploration including statistical enrichment of sets of genes or other entities. InterMine databases have been developed for the major model organisms, budding yeast, nematode worm, fruit fly, zebrafish, mouse, and rat together with a newly developed human database. Here, we describe how this has facilitated interoperation and development of cross-organism analysis tools and reports. InterMine as a data exploration and analysis tool is also described. All the InterMine-based systems described in this article are resources freely available to the scientific community.

Colour analysis of the equine endometrium: comparison of spectrophotometry and computer-assisted analysis of photographs within the L*a*b* colour space system

The aims of this study were to compare two different methods of quantifying the colour of the luminal surface of the equine endometrium and to relate the results to histopathological evidence of inflammation and fibrosis. The mucosal surfaces of 17 equine uteri obtained from an abattoir were assessed using a spectrophotometer and by computer-assisted analysis of photographs. Values were converted into L(*)a(*)b(*) colour space. Although there was significant correlation between the two methods of quantification, variations in 'brightness', 'red' and 'yellow' values were noted. Within a given uterus, measurements using the spectrophotometer did not differ significantly. Using photographic analysis, brightness differed between horns, although no differences in chromaticity were found. Histopathological classification of changes within endometria corresponded to measured differences in colour. Extensive fibrosis was associated with increased brightness and decreased chromaticity using both methods. Inflammation correlated with reduced chromaticity, when measured by spectrophotometry, and with reduced brightness and yellow values, when assessed photographically. For this technique to gain wider acceptance as a diagnostic tool, e.g. for the endoscopic evaluation of uterine mucosae in vivo, standardised illumination techniques will be required so that colours can be compared and interpreted accurately.

CD 45/67 immunobead preparation of human semen activates granulocytes

In human semen reactive oxygen species (ROS) produced by spermatozoa or leukocytes can impair spermatozoa functions. The objective of this study was to assess the effects of CD 45- and/or CD 67 immunobead preparation on the chemiluminescence (CL) of seminal plasma free ejaculate cells (= original cell suspension), as well as of the spermatozoa and leukocyte fractions. The original cell suspensions of 68 infertile and 8 fertile men were incubated with CD 45 or CD 67 immunobeads. After separation in a magnetic field the luminol chemiluminescence of the original cell suspensions, the spermatozoa and the leukocyte fractions were recorded on a luminometer. Spermatozoa fractions did not contain any leukocytes as no increase in CL-counts occurred after addition of FMLP. The number of peroxidase-positive cells (per 10(7) spermatozoa) correlated with the CL of the original cell suspensions (r = 0.7; P < 0.0001) as well as the CL of the spermatozoa and the leukocyte fractions after CD 45 or CD 67-preparation (r = 0.64; P < 0.0001). The CL of the spermatozoa and of the leukocyte fractions after CD 45 immunobead incubation were significantly correlated (r = 0.091; P < 0.0001). According to these data contaminating leukocytes could be eliminated by immunobead preparation. However, incubation of original cell suspensions with CD 45 or CD 67 immunobeads stimulated leukocytes to release soluble products resulting in elevated CL signals both in the leukocyte and the spermatozoa fractions. These effects have to be taken into account when using immunobeads for the preparation of human semen.