Platelet-biased hematopoietic stem cells (PLT-HSCs) play key roles in normal physiology, aging, and blood cancer. However, currently, no markers allow their accurate identification or prospective isolation. We here combine single-mouse hematopoietic stem cell (HSC) gene expression, chromatin accessibility, and surface proteome profiling to identify subtype-specific markers. Using machine learning, we identified markers (CD61hiCD274hiCD357loCD27lo) that isolate PLT-HSCs to high purity, validated by single-cell transplantation. Furthermore, we develop a minimal expression marker panel that discriminates PLT- and multi-lineage (MUL-)HSCs using microfluidics-based single-cell RT-qPCR. We show that both methods detect the age-associated increase in PLT-HSCs, while poly(I-C)-induced chronic inflammation did not alter HSC lineage bias. In contrast, romiplostim treatment increased MUL-HSC prevalence. Finally, using spectral flow cytometry to simultaneously quantify cell cycle and HSC lineage bias, we show that platelet depletion selectively activates PLT-HSCs. Together, these approaches allow accurate isolation of PLT-HSCs and robust quantification of lineage bias under perturbation.