Summary.We compared the expression of matrix metalloproteinases (MMP-2 and MMP-9) and tissue inhibitors of metalloproteinases (TIMP-1 and TIMP-2) in bone marrow acute myelogenous leukaemia (AML) blasts and leukaemic cell lines (HEL, HL-60, K-562 and KG-1) with their expression in normal bone marrow cells. All AML samples and leukaemic cell lines tested expressed MMP-9 and/or MMP-2 mRNA and, accordingly, these gelatinases were secreted into media. Moreover, TIMP-1 and TIMP-2 mRNA and secreted proteins were demonstrated in all the AML samples. Although all the leukaemic cell lines expressed TIMP-1, the HL-60 cells also expressed TIMP-2. In contrast, normal steady-state bone marrow immature progenitor cells (CD34 þ cells) did not express or secrete either MMP-2 or MMP-9, but more mature mononuclear cells from normal bone marrow expressed and secreted MMP-9. Also, normal bone marrow CD34 þ cells and mononuclear cells expressed TIMP-1 and TIMP-2 mRNA, but these proteins were not detectable by reverse zymography. Furthermore, whereas bone marrow fibroblasts and endothelial cells secreted only latent MMP-2, the activated form of this enzyme was found in media conditioned by cells obtained from long-term cultures of normal and AML bone marrow adherent layers. Our finding of up-regulated production of gelatinases, TIMP-1 and TIMP-2 by leukaemic cells suggests that these proteins may be implicated in the invasive phenotype of AML.

We construct equivariant Khovanov spectra for periodic links, using the Burnside functor construction introduced by Lawson, Lipshitz, and Sarkar. By identifying the fixed-point sets, we obtain rank inequalities for odd and even Khovanov homologies, and their annular filtrations, for prime-periodic links in S 3 .

For a 2-periodic linkL in the thickened annulus and its quotient link L, we exhibit a spectral sequence with. This spectral sequence splits along quantum and sl 2 weight space gradings, proving a rank inequality rk F2 AKh j,k (L) ≤ rk F2 AKh 2j−k,k (L) for every pair of quantum and sl 2 weight space gradings (j, k). We also present a few decategorified consequences and discuss partial results toward a similar statement for the Khovanov homology of 2-periodic links.

Progenitor cells play fundamental roles in preserving optimal organismal functions under normal, aging, and disease conditions. However, progenitor cells are incompletely characterized, especially in the brain, partly because conventional methods are restricted by inadequate throughput and resolution for deciphering cell-type-specific proliferation and differentiation dynamics in vivo. Here, we developed TrackerSci, a new technique that combines in vivo labeling of newborn cells with single-cell combinatorial indexing to profile the single-cell chromatin landscape and transcriptome of rare progenitor cells and track cellular differentiation trajectories in vivo. We applied TrackerSci to analyze the epigenetic and gene expression dynamics of newborn cells across entire mouse brains spanning three age stages and in a mouse model of Alzheimer's disease. Leveraging the dataset, we identified diverse progenitor cell types less-characterized in conventional single cell analysis, and recovered their unique epigenetic signatures. We further quantified the cell-type-specific proliferation and differentiation potentials of progenitor cells, and identified the molecular programs underlying their aging-associated changes (e.g., reduced neurogenesis/oligodendrogenesis). Finally, we expanded our analysis to study progenitor cells in the aged human brain through profiling ~800,000 single-cell transcriptomes across five anatomical regions from six aged human brains. We further explored the transcriptome signatures that are shared or divergent between human and mouse oligodendrogenesis, as well as the region-specific down-regulation of oligodendrogenesis in the human cerebellum. Together, the data provide an in-depth view of rare progenitor cells in mammalian brains. We anticipate TrackerSci will be broadly applicable to characterize cell-type-specific temporal dynamics in diverse systems.

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