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Analytical chemistry tools help turn biological samples into measurements clinicians can use. Sequencing can reveal genetic variants; mass spectrometry and chromatography can measure molecules; and spectroscopy, imaging and microfluidics can support other kinds of analysis. These technologies are helping advance personalised medicine, but a promising measurement becomes useful in care only when the test is reliable and its result is meaningful for the clinical decision.
What personalised diagnostics are designed to do
Precision medicine uses differences between people—including genes, environment and lifestyle—to inform prevention and treatment. In some cancers, molecular testing already helps clinicians select treatment based on a tumour’s molecular profile. The point is not simply to collect more data: it is to identify information that can guide a particular decision.
A biomarker is a measurable biological feature that may help detect a disease, assess its progression or identify a possible targeted therapy. Its presence alone does not establish that a treatment will work or that a test is useful. The measurement must be dependable, and the result must be interpreted in the context of the test’s intended use.
Which analytical tools contribute, and how
Different methods measure different signals. No single platform is best for every sample or clinical question.
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| Tool | What it measures or contributes | Clinical context and status |
|---|---|---|
| Next-generation sequencing (NGS) | Reads large portions of genetic material to identify variants. | Used in molecular testing that can inform diagnosis and treatment, including in precision oncology. Results depend on test performance and variant interpretation. |
| Mass spectrometry | Identifies or measures molecules in a sample. | Specialised diagnostic applications include therapeutic drug monitoring and precision dosing. A 2026 status review describes diagnostic use as limited but growing, not universally available. |
| Chromatography and related protein or small-molecule analysis | Separates sample components so they can be analysed; may be paired with mass spectrometry or other methods. | Can support biomarker discovery and measurement. Detecting a candidate biomarker does not by itself establish clinical usefulness. |
| Spectroscopy | Characterises materials by how they interact with light or other electromagnetic signals; examples include Raman, UV-Vis and fluorescence methods. | Measurement frameworks include quality control in point-of-care pharmaceutical manufacturing. This is not evidence that these methods are broadly deployed as personalised diagnostic tests. |
| Microfluidics and biofluidics | Handle small sample volumes and combine them with sensing, potentially on a chip. | Lab-on-a-chip and point-of-care molecular analysis are technology-development areas. Compact sample-to-answer workflows are a potential, not proof of routine availability for a particular test. |
| Imaging and spatial analysis | Relate molecular measurements to where features occur in tissue, including high-resolution tumour mapping. | Integration of imaging with molecular analysis, proteomics or mass spectrometry is an area of development and translation. |
| Bioinformatics | Processes and interprets complex data, including sequencing results, using computational tools and variant databases. | Part of the NGS testing ecosystem; the sequencer alone does not produce a clinically interpreted result. |
How to judge whether a test fits a clinical question
Comparing instruments in isolation can be misleading. A useful comparison starts with the decision the clinician needs to make and checks whether the measurement can support it.
- Identify the target. Specify the analyte, genetic variant or other biomarker the test is intended to detect or measure.
- Check analytical performance for the intended use. Ask whether the test measures that target accurately and reliably in the relevant sample. Performance for one purpose does not automatically establish performance for another.
- Consider the sample and its preparation. Sample type, collection and processing affect what can be measured and how consistently.
- Match timing and throughput to the decision. The required turnaround and number of samples may favour a different workflow from one chosen solely for technical capability.
- Establish where the workflow happens. A central laboratory and a point-of-care setting have different practical requirements. A small device or a chip-based concept does not, on its own, demonstrate that a test is clinically available at the point of care.
- Ask whether the result is clinically meaningful. A technically sound measurement still needs evidence that its interpretation is relevant to diagnosis, treatment or another defined clinical decision.
Why validation and measurement infrastructure matter
Test quality has at least two distinct dimensions: whether the assay measures its target reliably and whether the result is clinically meaningful for the intended use. FDA describes analytical validation and clinical validity as separate considerations in the development and oversight of NGS tests. Genetic testing also creates interpretation challenges because sequencing produces complex data; bioinformatics tools, standards and variant databases are part of the broader testing system.
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FDA issued final guidance documents in April 2018 addressing clinical validity of genetic variant databases and analytical validation of NGS tests for suspected germline disease. The agency has also noted that standards and performance metrics must evolve as the field changes. Those documents are a dated regulatory milestone, not a substitute for checking current guidance when assessing a specific test or making a current regulatory claim.
Measurement assurance extends beyond an individual laboratory. NIST’s precision medicine work supports measurement methods, reference materials and interlaboratory testing across areas including genomics, cancer biomarkers, the microbiome and flow cytometry. Shared measurement infrastructure helps address whether results are comparable and dependable across methods and laboratories; a biomarker association alone is not sufficient evidence to guide care.
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- Wide Range of Applications: these beakers and graduated cylinders are practical gifts for experimenters and science, very suitable for projects, laboratories, science researches and so on
What the point-of-care future does—and does not—mean
Microfluidics and biofluidics seek to combine small-volume samples—such as blood, saliva, urine, sweat or tears—with sensing approaches that may include spectroscopy, electrochemical signals, colour changes or imaging. The aim is to make compact workflows that move from sample handling toward an answer with fewer steps. NCI lists lab-on-a-chip and point-of-care systems among technologies for molecular analysis, while a 2026 review describes biofluidic diagnostic concepts.
These developments indicate a direction for research and technology translation, not blanket clinical availability. Separately, NIST’s point-of-care pharmaceutical manufacturing programme concerns measurement science for manufacturing control, including analysis of ingredients and finished dosage forms. That work shows how analytical methods can support product quality; it should not be mistaken for evidence that personalised diagnostic testing is already decentralised across care settings.
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What to expect from analytical chemistry in personalised care
The clearest contribution is the measurement layer: tools make it possible to detect, quantify or map biological features that may help answer clinical questions. Sequencing, mass spectrometry, chromatography, spectroscopy, imaging and chip-based sensing each offer different capabilities, and computational interpretation can be essential to turning results into usable information.
The future depends not only on more sensitive instruments or smaller devices, but also on validation, meaningful clinical interpretation and measurement systems that produce trustworthy results. That is why established uses—such as molecular testing that informs some cancer treatment choices—should be distinguished from emerging prototypes and point-of-care concepts still being developed.
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