Clinical Phenotyping
Clinical Phenotyping - Related Videos
14:45
Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
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Cited by 14 •
2012
In this report, we demonstrate the staining and analysis steps of a phenotyping assay performed on fresh whole blood to enumerate major innate and adaptive leukocyte populations. We emphasize considerations for performing these procedures in the context of a multicenter clinical trial.
07:35
A Non-invasive Way to Isolate and Phenotype Cells from the Conjunctiva
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Cited by 5 •
2017
The exposed normal ocular surface consists of cornea and conjunctiva. Epithelial cells, goblet cells and immune cells are present in the conjunctiva. Here, a non-invasive, technique of impression cytology is described using an impression cytology device and flow cytometry to analyze immune cells in the conjunctiva.
09:40
RNAi Screening to Identify Postembryonic Phenotypes in C. elegans
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Cited by 9 •
2012
We describe a sensitized method to identify postembryonic regulators of protein expression and localization in C. elegans using an RNAi-based genomic screen and an integrated transgene that expresses a functional, fluorescently tagged protein.
12:43
On-Chip Endothelial Inflammatory Phenotyping
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Cited by 3 •
2012
Microfluidic flow chambers etched by photolithography and fabricated from PDMS are applied to probe functional outcomes associated with EC dysfunction and inflammation. In a representative experiment, the ability of differential shear stress to modulate monocytic cell adhesion to cytokine activated EC monolayers is demonstrated.
07:35
Use of Ultra-high Field MRI in Small Rodent Models of Polycystic Kidney Disease for In Vivo Phenotyping and Drug Monitoring
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Cited by 14 •
2015
The use of ultra-high field MRI as a non-invasive way to obtain phenotypic information of rodent models for polycystic kidney disease and to monitor interventions is described. Compared with the traditional histological approach, MRI images can be acquired in vivo, allowing for longitudinal follow-up.
FAQs
It compares combinations of symptoms, examination findings, laboratory results, imaging features, and disease manifestations rather than relying on a single observation. Recurrent patterns can reveal clinically meaningful variation among patients who share a broad diagnosis. Recognizing these subtypes may improve risk assessment, prognosis, and treatment selection by aligning decisions with the patient’s specific presentation.
Standardization makes patient characteristics more consistent and comparable across clinical settings and research studies. It helps reduce ambiguity in how symptoms, test results, and disease features are recorded, allowing investigators to identify patterns more reliably. Consistent data also strengthens clinical trials, supports patient stratification, and makes relationships between clinical findings and biological measurements easier to evaluate.
Researchers can compare structured clinical traits with genomic or molecular observations to determine whether particular biological findings occur alongside specific disease manifestations or subtypes. This connection helps organize patient groups according to both observable characteristics and underlying biology. The resulting associations can support precision medicine by linking individual presentations with more tailored research or treatment strategies.
Patients with the same broad condition may show different symptoms, test findings, imaging patterns, or other manifestations. Documenting these differences over time allows clinicians and researchers to relate particular phenotypic patterns to progression and prognosis. This approach can reveal which patient characteristics are associated with changing disease status and can support more informed risk assessment.
A workflow may combine the medical history, clinical examination, diagnostic testing, laboratory findings, imaging, symptoms, and documented disease manifestations. These sources are considered together to characterize the patient rather than treating each result as an isolated fact. The combined record provides the basis for identifying patterns, comparing patients, and tracking changes during evaluation or follow-up.
Researchers may use it to stratify participants into clinically meaningful groups, distinguish disease subtypes, or relate patient characteristics to genomic and molecular results. It is also useful for improving the design and interpretation of clinical trials because standardized phenotypic information helps define comparable participant groups. These applications support analysis of variation in prognosis, disease progression, and treatment response.
By organizing a patient’s symptoms, examination findings, tests, imaging, and disease manifestations into clinically meaningful patterns, phenotyping can provide a more detailed basis for medical decisions. Clinicians may use those patterns for diagnosis, risk assessment, prognosis, and treatment selection. In research, the same framework advances precision medicine by connecting individual patient characteristics with biological and clinical outcomes.