Background: Plasma biomarkers showed a promising value in the disease diagnosis and management of Alzheimer’s disease (AD). However, profiles of the biomarkers and the associations with cognition across a spectrum of cognitive stages have seldom been reported. Methods: We recruited 320 individuals with cognitive impairment and 131 cognitively normal participants from a memory clinic and a community cohort. Participants were classified into 6 groups based on their Clinical Dementia Rating (CDR) scores and clinical diagnosis, including AD, amnestic mild cognitive impairment (aMCI), and normal cognition (NC). A battery of neuropsychological tests was used to assess the global and domain-specific cognition. Plasma Aβ_1-40, Aβ_1-42, Aβ_1-42/Aβ_1-40, total tau (t-tau), neurofilament protein light chain (NfL), and phosphorylated tau at threonine 181 (p-tau181) were quantified using the single-molecule array (Simoa) platform. Results: All the plasma markers (Aβ_1-40, Aβ_1-42, Aβ_1-42/Aβ_1-40, t-tau, NfL, p-tau181) showed certain discrepancies among NC, aMCI, and AD groups. The p-tau181 level showed a continuous escalating trend as the CDR scores increased from 0 (NC group) to 3 (severe AD). Compared with other biomarkers, p-tau181 had correlations with broader cognitive domains, covering global cognition (r = −0.536, P < 0.0001), memory (r = −0.481, P < 0.0001), attention (r = −0.437, P < 0.0001), visuospatial function (r = −0.385, P < 0.0001), and language (r = −0.177, P = 0.0003). Among participants with CDR ≥ 1, higher p-tau181 was correlated with worse global cognition (r = −0.301, P < 0.001). Conclusions: Plasma p-tau181 had correlations with broader cognitive domains, suggesting its potential as a promising clinical-relevant blood-based biomarker.

Pancreatic cancer is a highly lethal malignancy with poor prognosis due to the lack of early symptoms and resultant late diagnosis. Thus, it is extremely urgent to establish a simple and effective method for the early diagnosis of pancreatic cancer. Although some studies have provided positive evidence for the use of exosomal surface protein glypican-1 (GPC1) as a biomarker for early screening, its clinical application is still controversial. Here, we systematically verified the role of exosomal GPC1 as a potential screening biomarker. First, bottleneck problems of a stable detection method and an identification standard were systematically studied, and a Python-based standardized data processing method was established to analyze exosomal GPC1 expression. Second, a detection panel consisting of exosomal GPC1, exosomal cluster of differentiation 82 (CD82), and serum carbohydrate antigen 19-9 (CA19-9) was employed for pancreatic cancer detection. This panel exhibited excellent diagnostic results (AUC = 0.942) and could effectively distinguish healthy people from patients with pancreatic cancer (P value threshold = 0.2282) and patients with pancreatitis from patients with pancreatic cancer (P value threshold = 0.5467). Implications: These results indicate that the combined detection of exosomal GPC1, exosomal CD82, and serum CA19-9 shows great promise as a standard method for pancreatic cancer detection and that this panel could be further applied for screening pancreatic cancer in Chinese populations.

The detection of low-abundance proteins in physiological samples is of high importance in clinical diagnostics and pathology research. However, protein biomarkers that can be accurately quantified by the existing detection methods and applied to clinical practice are just the tip of the iceberg. Quite a few proteins with concentrations below pg mL⁻¹, which are closely related to the occurrence and progression of diseases, are not effectively utilized due to the limitations of detection technology. In addition, simultaneously measuring multiple proteins in a single reaction improves the diagnostic accuracy and reliability since most diseases will cause abnormal changes in multiple biomarkers. Therefore, increasing demands to realize ultrasensitive and multiplexed protein detection have promoted the rapid development of digital immunoassay recently. Furthermore, the high-end digital detection is also expected to spark its potential for streamlined and integrated workflows in clinical applications, extending beyond its use in fundamental research within central laboratories. In this review, we focus on elucidating the core strategies and approaches to enhance the performance of digital immunoassay across three key dimensions: sensitivity, multiplexing, and clinical accessibility. Ultimately, we highlight the promising prospects of digital protein detection in the next decade.