Oligodendrocytes (OLs) are an important class of macroglia responsible for producing the myelin sheaths that insulate and protect neuronal axons. Forebrain OLs arise from multiple embryonic origins. Previous fate-mapping study using Nkx2.1^Cre (Xu et al., 2008), Gsh2^Cre (Kessaris et al., 2006), and Emx1^Cre (Gorski et al., 2002) reported consecutive and competing waves of OLs derived from medial ganglionic eminence/preoptic area (MGE/POA), lateral/caudal ganglionic eminences (LGE/CGE), and dorsal pallium (Kessaris et al., 2006). The first wave of OLs generated by MGE/POA (_MPOLs) was believed to be eliminated postnatally, while those from the second and third waves (_LCOLs and dOLs) survive and populate the cortex and corpus callosum at comparable proportions. Several other studies provided both supporting and contradicting evidence to this model (Nakahira et al., 2006; Tsoa et al., 2014; Naruse et al., 2016; Orduz et al., 2019; Liu et al., 2021; Shen et al., 2021; Tripathi et al., 2011; Winkler et al., 2018). Moreover, Gsh2 was recently found to be expressed in dorsal progenitors (Zhang et al., 2020), casting doubt on the interpretation of lineage tracing data from Gsh2^Cre.

In this study, we generated new genetic tools and combinatorial fate-mapping strategies which allow direct visualization and comparison among OLs derived from different origins. We found that neocortical OLs are primarily composed of dOLs, rather than similar proportions of _LCOLs and dOLs. In contrast, _LCOLs and dOLs made comparable contributions to piriform cortex. We also found that although _MPOLs only make a small contribution, they do persist in the cortex beyond adulthood with a unique spatial pattern distinct from that of the dOLs. In the two major white matter commissure tracts, dOLs are the vast majority in corpus callosum but make little contribution to anterior commissure, while _LCOLs behaved the opposite. These findings significantly revised the classical view and provided a new and more comprehensive picture of cortical and white matter OL origins.

To unambiguously track OLs from different embryonic origins, we first generated a knock-in driver, Opalin^{P2A-Flpo-T2A-tTA2} (Figure 1), orthogonal to Cre drivers that label dorsal or ventral progenitors (Progenitor^Cre). Opalin (also known as Tmem10) encodes oligodendrocytic myelin paranodal and inner loop protein that are specifically expressed in differentiated OLs (Kippert et al., 2008; Yoshikawa et al., 2008; Jiang et al., 2013; Marques et al., 2016). In Opalin^{P2A-Flpo-T2A-tTA2}, Flpo and tTA2 were inserted before the STOP codon and linked by self-cleavage peptide P2A and T2A (Figure 1A–C), allowing co-transcription and translation with Opalin. Flp-mediated recombination by this driver (hereinafter referred to as Opalin^Flp for simplicity) enables highly specific, efficient, and irreversible OL labeling, while the tTA2 component offers the flexibility for OL-specific labeling in tunable densities (Figure 1D–F).

Figure 1

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Figure 1—source data 1

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Uncropped and labeled blot for Figure 1B.

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Figure 1—source data 5

The raw data for the visualization of data presented in Figure 1F.

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Next, we established two types of genetic combinatorial fate-mapping strategies to directly visualize OLs from different embryonic origins (Figure 2): (1) combining Opalin^Flp and Progenitor^Cre with intersectional reporters Ai65 to label OLs derived from Cre+ progenitor domain by RFP (Figure 2A); (2) combining Opalin^Flp and Progenitor^Cre with RC::FLTG (Plummer et al., 2015) to simultaneously label OLs derived from Cre+ progenitors by green fluorescent protein (GFP) and OLs derived from the complementing Cre− progenitors by RFP (Figure 2B, Figure 2—figure supplement 1A). The first approach allowed us to track dOLs and _MPOLs (Figure 2C, E). The second approach empowered us to observe and compare OLs generated from dorsal and ventral origins (Figure 2—figure supplement 1B), or those from Gsh2+ and Gsh2− progenitors, in the same brain (Figure 2—figure supplement 1C). Importantly, the subtraction power enabled us to target OLs derived from LGE/CGE progenitors that express neither Emx1 nor Nkx2.1 (Figure 2D and Figure 2—figure supplement 1D). In addition, these strategies greatly facilitated the identification of OLs derived from specific origin which exist at relatively low density in certain regions.

Figure 2 with 3 supplements see all

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The raw data for the visualization of data presented in Figure 2G–J, N.

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Deploying these strategies, we assessed the differential contributions of dOLs, _LCOLs, and _MPOLs by analyzing RFP+ cells in the following mice: Opalin^Flp::Emx1^Cre::Ai65 (Figure 2C), Opalin^Flp::Emx1^Cre::Nkx2.1^Cre::RC::FLTG (Figure 2D), and Opalin^Flp::Nkx2.1^Cre::Ai65 (Figure 2E). To better assess their contributions to the total OL population (Figure 2F), we co-stained RFP with the mature OL marker aspartoacylase (ASPA) (Huang et al., 2023; Figure 2G–I) and quantified the ratio of co-localization (Figure 2J). Notably, all RFP+ cells are ASPA+, reassured the specificity of our label strategies. We observed two significant differences from the traditional model in the neocortex. The first major deviation is that, instead of comparable contributions by dOLs and _LCOLs, the vast majority of neocortical OLs were dOLs but not _LCOLs. The densities (Figure 2G–I and Figure 2—figure supplement 2A–F) and ASPA ratios (Figure 2J) of dOLs are much higher than those of _LCOLs. Considering the possibility of incomplete recombination in combinatorial reporters, and the relatively low Cre activity in the dorsal MGE of Nkx2.1^Cre (Xu et al., 2008), the genuine contribution of _LCOLs to the neocortex could be even lesser than our current observation. Therefore, the large quantity of neocortical OLs labeled by Gsh2^Cre in previous study (Kessaris et al., 2006) or by GFP in Opalin^Flp::Gsh2^Cre::RC::FLTG (Figure 2—figure supplement 1C) most likely were predominantly dOLs generated by Gsh2+ dorsal progenitors (Zhang et al., 2020), rather than bona fide _LCOLs.

The second major deviation is that cortical _MPOLs are not completely depleted postnatally. Instead, they make a small but continued contribution with a unique spatial distribution pattern (Figure 2I, J and Figure 2—figure supplement 2D–G). _MPOLs display a clear rostrocaudal density decline (Figure 2—figure supplement 2E, F), a higher density in somatosensory cortex (SS) than motor cortex (Mo) (Figure 2I), and a laminar preference toward layer 4 (L4) in SS (Figure 2—figure supplement 2G). In contrast, the distribution of dOLs and _LCOLs do not vary significantly across the rostrocaudal axis (Figure 2—figure supplement 2E, F) or between Mo and SS (Figure 2G, H), but exhibits increased density toward deeper layers (Figure 2—figure supplement 2G). Importantly, we have observed cortical _MPOLs in mice as old as 1 year (Figure 2—figure supplement 2H), well beyond the age analyzed in previous reports (Kessaris et al., 2006; Orduz et al., 2019; Liu et al., 2021), suggesting a persisted contribution.

We then turned our attention to the lateral three-layer archicortex, piriform cortex (Pir). Different from the neocortex, Pir contains higher proportions (Figure 2J) of _LCOLs than dOLs. _MPOLs make the lowest contribution (Figure 2J) at a density similar to SS and higher than Mo (Figure 2I).

These combinatorial models also grant us the opportunity to revisit the differential contributions of dOLs, _LCOLs, and _MPOLs to the two commissural white matter tracts, corpus callosum (cc), and anterior commissure (ac), which contain high density of OLs (Figure 2K–M). We found that, similar to the neocortex, cc is mainly populated by dOLs and supplemented by very low proportions of _LCOLs and _MPOLs (Figure 2K–N). Interestingly, _LCOLs and _MPOLs seem to show preferential distribution in the lateral and medial regions of cc (cc-l and cc-m), respectively (Figure 2L–N). Different from cc, ac is mainly populated by _LCOLs and _MPOLs and supplemented by very low proportion of dOLs (Figure 2K–N).

To substantiate the above results, we further breed Opalin^Flp::Emx1^Cre::Nkx2.1^Cre::Ai65 to label dOLs together with _MPOLs by RFP and co-stained them with ASPA (Figure 2—figure supplement 3). RFP−ASPA+ cells were difficult to find in Mo, SS, and cc, but were more easily observed in Pir and ac, consistent with the respective low and high _LCOL contributions in these regions.

In summary, our findings significantly revised the canonical model of forebrain OL origins (Figure 3A), and provided a new and more comprehensive view (Figure 3B). We demonstrated that neocortical OLs are mainly derived from dorsal origin with small but lasting contribution from the ventral origin (Figure 2, Figure 2—figure supplements 1B and 2). Our data showed that LGE/CGE makes little contribution to neocortex and cc, but makes major contribution to piriform cortex and ac (Figure 2 and Figure 2—figure supplement 3). This finding is supported by another report in which in utero electroporation failed to label LGE-derived cortical OLs in both embryonic and early postnatal brains, and an exclusion strategy revealed very low percentage of LGE/CGE-derived cortical OLs in neonatal brains (Li et al., 2023). The lack of adult labeling in our study together with the lack of developmental labeling in the other study suggests that the lack of _LCOL in neocortex is less likely caused by competitive postnatal elimination, but more likely due to limited production and/or allocation. We further discovered that MGE/POA makes a small but persistent contribution to the neocortex with a distinct distribution pattern featured by a rostral-high to caudal-low gradient and a preference toward L4 in SS (Figure 2—figure supplement 2). Whether their enduring existence and highly biased localization has functional implications awaits future exploration. In addition, we found that the cc showed a similar OL composition as the neocortex, but the Pir and the ac each exhibited distinct OL compositions in term of their embryonic origins. _LCOLs are the major contributor to both regions, while dOLs and _MPOLs mainly contribute to Pir and ac, respectively (Figure 2).

Figure 3

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In addition to the new framework of forebrain OL origins (Figure 3), we also generated a new driver (Figure 1) and established multiple combinatorial genetic models (Figure 2) for efficient tracking and direct visualization of OLs from different embryonic origins without interference from other cells types sharing the same progenitor domains such as OL precursors, astrocytes, and neurons (Figures 1–2). These tools set up a firm foundation and will provide reliable experimental access for future inquiries on the development and function of diverse OLs in healthy and disease brains (Gong et al., 2022), especially to uncover the relationship between their developmental origins and the functional and molecular heterogeneity.

All data generated or analyzed during this study are included in the manuscript. Source data have been provided for Figures 1 and 2.

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Author details

1. Yuqi Cai

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Zhirong Zhao and Mingyue Shi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0001-5831-4980

2. Zhirong Zhao

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Validation, Investigation, Writing - review and editing

   Contributed equally with

   Yuqi Cai and Mingyue Shi

   Competing interests

   No competing interests declared

3. Mingyue Shi

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Data curation, Validation, Investigation, Writing - review and editing

   Contributed equally with

   Yuqi Cai and Zhirong Zhao

   Competing interests

   No competing interests declared

4. Mingfang Zheng

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

5. Ling Gong

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Formal analysis, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration

   Competing interests

   No competing interests declared

6. Miao He

   Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   hem@fudan.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0731-6801

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