Sickle cell disease (SCD) is a common genetic disorder in Africa. Some ongoing work in SCD research includes the analysis and comparisons of variation in phenotypic presentations and disease outcomes with the genotypic signatures. This has contributed to the observed growth of molecular and genetic data in SCD. However, while the “omics” data continues to pile, the capacity to interpret and turn the genetic findings into clinical practice is still underdeveloped, especially in the developing region. Building bioinformatics infrastructure and capacity in the region is key to bridging the gap. This paper seeks to illustrate how the Sickle Cell Programme (SCP) at the Muhimbili University of Health and Allied Sciences (MUHAS) in Tanzania, modeled the integration of infrastructure for bioinformatics and clinical research while running day-to-day clinical care for SCD in Tanzania.

Genetic modifiers underlying various sickle cell disease phenotypic expressions are largely unexplored in Africa due to lack of databases linking biospecimens with demographic and clinical data. The problem may be compounded by a complete lack of biorepositories in these settings. This article aims to document the physical verification of biospecimens stored in the biorepository and link them to patient clinical and demographic information to facilitate plans for genomic and related clinical research studies. We reviewed and updated the existing biorepository infrastructure at Muhimbili Sickle Cell Programme in Dar es Salaam, Tanzania. The database of archived biospecimens was updated with the location information of respective biospecimens following the physical verification of biospecimens and then mapping the patient demographic and clinical data with the biospecimen data using sickle cell patients’ demographic identifiers. Three freezers maintained at -80°C store a total of 74,079 biospecimens, of which 63,345 were from 5,159 patients registered in the Muhimbili Sickle Cohort from 2004 to 2016. Out of stored biospecimens, follow-ups were 46,915 (74.06%), control 8,067 (12.74%), admission 5,517 (8.71%) and entry 2,846 (4.49%). Of these registered patients, females were 2,521 (48.87%) and males were 2,638 (51.13%). The age distribution was 1 to 59 years, with those above 18 years being 577 (11.18%) and children 4,582 (88.82%) of registered patients. The notable findings during the process include a lack of automated biospecimen checks, laboratory information management system and standardization of equipment used, biospecimens not linked to clinical and demographic data, date format inconsistencies, lack of regular updating of a database on exhausted biospecimens and updates when biospecimens are moved between positions within freezers. Well-organized biorepository plays a crucial role in answering future research questions. Enforcing strict standard operating procedures and quality control standards will ensure that laboratory scientists and other users adhere to the best biospecimen management procedures.