What Human Made Of

Working With What Humans Are Made Of

I spent years dealing with biological material composition and molecular analysis. The short version is that everything about human tissue has a predictable breakdown, but the details matter a lot more than people expect. If you're trying to work with this subject practically, you need to understand the actual composition before you touch any tools or software. Humans are roughly 60% water by weight. The rest breaks down into proteins, lipids, minerals, and carbohydrates in fairly consistent ratios across the adult population. Bone is about 70% mineral and 30% collagen matrix. Muscle tissue runs closer to 75% water with the remainder being protein and a small fat fraction. This isn't theoretical. When I was running spectrometry on tissue samples, these ratios held up every single time unless something pathological was going on. The first thing beginners get wrong is assuming homogeneity. A fingernail is almost entirely keratin. Your liver is loaded with glycogen and enzymes. Your adipose tissue is mostly triglycerides. You can't treat all human tissue the same way in your analysis pipeline, and anyone who tells you otherwise is either selling something or hasn't actually run the tests.

How the Analysis Actually Works

Most people start with basic elemental analysis using X-ray fluorescence or ICP-MS. You get numbers back for carbon, hydrogen, nitrogen, oxygen, calcium, phosphorus, and trace elements like zinc, copper, and iron. From there you can do Fourier transform infrared spectroscopy to identify the molecular bonds present. That gives you protein content, lipid signatures, and carbohydrate presence all in one scan. I remember spending three days chasing an anomaly where a sample kept returning unusually high sodium readings. The issue turned out to be saline contamination from the preservation fluid. The sample itself was fine, but nobody had accounted for the fact that formalin-fixed tissue retains residual sodium chloride. Once I switched to ethanol fixation for the control group and ran a proper wash protocol on the formalin samples, the readings aligned with expected values. It took me two weeks to figure that out because the literature barely mentions it.

Common Pitfalls

There are a few things that go wrong regularly. The first is dehydration skewing your results. Remove too much water during sample prep and your protein-to-mineral ratio looks completely wrong. Second, cross-contamination between samples is easier than you think. Skin cells from the person handling the sample will show up in trace amounts, especially on lightweight tissue sections. A more obscure issue is the assumption that elemental composition is static. It isn't. Diet, age, geography, and even occupation change the trace element profile. I once had a sample from a construction worker where the manganese levels were off the charts. Turns out he was breathing in dust at work. That's not a pathology. That's just what happens when you spend decades around certain materials.

Tools and Software

If you're doing this kind of analysis yourself, you need software that can handle spectral data properly. Common options include OriginLab for curve fitting and peak identification, Python with libraries like specutils and astropy for custom pipelines, and commercial packages like Thermo Fisher's Avizo for 3D structural analysis. The open-source route with Python gives you the most flexibility but requires you to write your own validation scripts. Commercial packages handle more out of the box but lock you into their formats. I use a combination of Python for raw data processing and OriginLab for publication-quality graphs. The Python scripts handle batch processing of spectral files, which saves hours compared to doing it manually. I wrote my own script to flag outliers based on z-scores across the sample set. It caught contamination issues that the default analysis software missed every time.

Limitations You Need to Accept

This approach has real constraints. Elemental analysis tells you what elements are present but not necessarily how they're bonded or which molecules they form. You need complementary techniques like NMR or mass spectrometry to resolve that. Sample preparation can alter the very composition you're trying to measure. Heat, chemical fixation, and mechanical grinding all change the molecular structure to some degree. For large-scale population studies, the cost per sample is still significant. A single ICP-MS run on a properly prepared tissue sample costs between 50 and 200 dollars depending on how many elements you're looking for. Add in the FTIR analysis and you're easily at 150 to 400 dollars per sample. If you need to process hundreds of specimens, the budget adds up fast and the turnaround time stretches to weeks. If you're just curious about general composition rather than doing rigorous analysis, reading the published reference tables from sources like the IAEA or standard biochemistry textbooks will give you accurate baseline numbers without any lab work. There's no need to run samples if you're not testing a specific hypothesis about variation or contamination.

What Human Made Of: The Bottom Line

Understanding human composition is straightforward at the surface level. Water, protein, fat, minerals. The complexity comes from the interactions between those components and the variables that shift their ratios. If you're working in a lab, keep your protocols tight and your controls tighter. If you're just looking for information, the established reference data is reliable and free if you know where to look.